This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.
IRJET- Implementation of Review Selection using Deep LearningIRJET Journal
This document presents a methodology for selecting reviews using deep learning. It involves collecting product reviews from websites, analyzing the reviews using part-of-speech tagging and developing a semantic classifier using Jaccard distance to match reviews to entity sets. A deep learning technique called Temporal Difference learning is then used to categorize reviews into 5 categories: Excellent, Good, Neutral, Bad, and Very Bad. This provides customers a more clear understanding of products compared to just star ratings. The methodology is aimed at helping customers make better informed purchase decisions based on categorized review sentiment.
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
This document summarizes a research paper that proposes three models for query expansion in a Hindi search engine: 1) Using lexical resources like HindiWordNet to find synonyms and related terms, 2) Using user context information like location, interests and profession, 3) Combining lexical resources and user context. An experiment compares the precision of results from simple Google searches to searches using each model. Precision was highest using the combined Model III at 0.79, showing that integrating lexical and user context information improves search quality in Hindi.
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
This document discusses sentiment analysis on unstructured product reviews. It begins with an introduction to sentiment analysis and opinion mining. The author then reviews related work on aspect-based sentiment analysis and feature extraction. The proposed work involves extracting features from unstructured reviews, determining sentiment polarity using SentiStrength, and classifying features using Naive Bayes. The experiment uses 575 reviews to identify prominent product aspects and determine sentiment scores. Naive Bayes classification is performed in Tanagra to obtain prior distributions of sentiment for each feature. Figures and tables are included to illustrate the process.
This document discusses various techniques for sentiment analysis of application reviews, including both statistical and natural language processing approaches. It describes how sentiment analysis can be used to analyze textual reviews and classify them as positive or negative. Several key techniques are discussed, such as using machine learning classifiers like Naive Bayes, extracting n-grams and sentiment-oriented words, and developing rule-based models using techniques like identifying parts of speech. The document also discusses using these techniques to perform sentiment analysis at both the document and aspect levels.
Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Using NLP Approach for Analyzing Customer Reviews cscpconf
The Web considers one of the main sources of customer opinions and reviews which they are
represented in two formats; structured data (numeric ratings) and unstructured data (textual
comments). Millions of textual comments about goods and services are posted on the web by
customers and every day thousands are added, make it a big challenge to read and understand
them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those
opinions and reviews. In this paper, we use natural language processing techniques to generate
some rules to help us understand customer opinions and reviews (textual comments) written in
the Arabic language for the purpose of understanding each one of them and then convert them
to a structured data. We use adjectives as a key point to highlight important information in the
text then we work around them to tag attributes that describe the subject of the reviews, and we
associate them with their values (adjectives).
IRJET- Implementation of Review Selection using Deep LearningIRJET Journal
This document presents a methodology for selecting reviews using deep learning. It involves collecting product reviews from websites, analyzing the reviews using part-of-speech tagging and developing a semantic classifier using Jaccard distance to match reviews to entity sets. A deep learning technique called Temporal Difference learning is then used to categorize reviews into 5 categories: Excellent, Good, Neutral, Bad, and Very Bad. This provides customers a more clear understanding of products compared to just star ratings. The methodology is aimed at helping customers make better informed purchase decisions based on categorized review sentiment.
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
This document summarizes a research paper that proposes three models for query expansion in a Hindi search engine: 1) Using lexical resources like HindiWordNet to find synonyms and related terms, 2) Using user context information like location, interests and profession, 3) Combining lexical resources and user context. An experiment compares the precision of results from simple Google searches to searches using each model. Precision was highest using the combined Model III at 0.79, showing that integrating lexical and user context information improves search quality in Hindi.
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
This document discusses sentiment analysis on unstructured product reviews. It begins with an introduction to sentiment analysis and opinion mining. The author then reviews related work on aspect-based sentiment analysis and feature extraction. The proposed work involves extracting features from unstructured reviews, determining sentiment polarity using SentiStrength, and classifying features using Naive Bayes. The experiment uses 575 reviews to identify prominent product aspects and determine sentiment scores. Naive Bayes classification is performed in Tanagra to obtain prior distributions of sentiment for each feature. Figures and tables are included to illustrate the process.
This document discusses various techniques for sentiment analysis of application reviews, including both statistical and natural language processing approaches. It describes how sentiment analysis can be used to analyze textual reviews and classify them as positive or negative. Several key techniques are discussed, such as using machine learning classifiers like Naive Bayes, extracting n-grams and sentiment-oriented words, and developing rule-based models using techniques like identifying parts of speech. The document also discusses using these techniques to perform sentiment analysis at both the document and aspect levels.
Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Using NLP Approach for Analyzing Customer Reviews cscpconf
The Web considers one of the main sources of customer opinions and reviews which they are
represented in two formats; structured data (numeric ratings) and unstructured data (textual
comments). Millions of textual comments about goods and services are posted on the web by
customers and every day thousands are added, make it a big challenge to read and understand
them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those
opinions and reviews. In this paper, we use natural language processing techniques to generate
some rules to help us understand customer opinions and reviews (textual comments) written in
the Arabic language for the purpose of understanding each one of them and then convert them
to a structured data. We use adjectives as a key point to highlight important information in the
text then we work around them to tag attributes that describe the subject of the reviews, and we
associate them with their values (adjectives).
Co-Extracting Opinions from Online ReviewsEditor IJCATR
Exclusion of opinion targets and words from online reviews is an important and challenging task in opinion mining. The
opinion mining is the use of natural language processing, text analysis and computational process to identify and recover the subjective
information in source materials. This paper propose a Supervised word alignment model, which identifying the opinion relation. Rather
than this paper focused on topical relation, in which to extract the relevant information or features only from a particular online reviews.
It is based on feature extraction algorithm to identify the potential features. Finally the items are ranked based on the frequency of
positive and negative reviews. Compared to previous methods, our model captures opinion relation and feature extraction more precisely.
One of the most advantages that our model obtain better precision because of supervised alignment model. In addition, an opinion
relation graph is used to refer the relationship between opinion targets and opinion words.
IRJET- Cross-Domain Sentiment Encoding through Stochastic Word EmbeddingIRJET Journal
This document discusses cross-domain sentiment encoding through stochastic word embedding. It proposes a novel method that takes advantage of stochastic embedding techniques to tackle cross-domain sentiment alignment in a simple way without complex model designs or additional learning tasks. The method encodes word polarity and occurrence information from reviews to learn representations across domains. It is benchmarked on sentiment classification tasks using two review corpora and compared to other classical and state-of-the-art methods.
A Review on Sentimental Analysis of Application ReviewsIJMER
As with rapid evolution of computer technology and smart phones mobile applications
become very important part of our life. It is very difficult for customers to keep track of different
applications reviews so sentimental analysis is used. Sentimental analysis is effective and efficient
evolution of customer’s opinion in real time. Sentimental analysis for applications review is performed
two approaches statistical model based approaches and Natural Language Processing (NLP) based
approaches to create rules. Two schemes used for analyzing the textual comments- aspect level
sentimental analysis analyses the text and provide a label on each aspect then scores on multiple
aspects are aggregated and result for reviews shown in graphs. Second scheme is document level
analyses which comprising of adjectives, adverbs and verbs and n-gram feature extraction. I have also
used our SentiWordNet scheme to compute the document-level sentiment for each movie reviewed
and compared the results with results obtained using Alchemy API. The sentiment profile of a movie is
also compared with the document-level sentiment result. The results obtained show that my scheme
produces a more accurate and focused sentiment profile than the simple document-level sentiment
analysis.
This document discusses various techniques for sentiment analysis of application reviews, including both statistical and natural language processing approaches. It describes how sentiment analysis can be used to analyze textual reviews and classify them as positive or negative. Several key techniques are discussed, such as using machine learning classifiers like Naive Bayes, extracting n-grams and sentiment-oriented words, and developing rule-based models using techniques like identifying parts of speech. The document also discusses using these techniques to perform sentiment analysis at both the document and aspect levels.
Sentimental analysis of audio based customer reviews without textual conversionIJECEIAES
The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.
MTVRep: A movie and TV show reputation system based on fine-grained sentiment ...IJECEIAES
Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.
Empirical Model of Supervised Learning Approach for Opinion MiningIRJET Journal
This summarizes an empirical model for opinion mining using supervised learning with an integrated alignment model and naive Bayesian classification model. The proposed model aims to automatically identify user reviews of products as positive or negative and provide an aggregated product rating based on review sentiment analysis and rankings. An alignment model is used to match keywords between source and target reviews to determine sentiment polarity. If a match is not found, the review is sent to a naive Bayesian classification model for sentiment analysis and rating. A rank aggregation model then considers data parameters like user ID, time, and rank to generate a ranked list of products based on ratings and sentiment analysis while excluding short-duration sessions or redundant comments. The proposed hybrid model aims to provide more accurate results for product sentiment analysis
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document summarizes a research paper that proposes a method for performing sentiment analysis on product reviews to identify promising product features. It involves scraping short reviews from websites, preprocessing the text through cleaning, tokenization and part-of-speech tagging. Next, it uses pattern mining and a custom lexicon dictionary to determine the overall sentiment score and sentiment scores for specific product features. The goal is to analyze which features consumers view most positively to help businesses understand customer preferences.
Web User Opinion Analysis for Product Features Extraction and Opinion Summari...dannyijwest
Selling the product through Web has become more popular because of online shopping. This enables
merchants to sell their products through Web and expects the customer to express their opinion through
online about the product which they have purchased. Due to this we find number of customer reviews on a
particular product, it varies from hundreds to thousands, for some product it is more than that. In order to
help the customer and the manufacture/merchant we propose a semantic based approach to mine different
product features and to find the opinion summarization about each of these extracted product features by
means of web user opinion expressed through the customer reviews using typed dependency relations.
IRJET- Survey of Classification of Business Reviews using Sentiment AnalysisIRJET Journal
1. The document discusses using machine learning algorithms like Naive Bayes and Linear SVC to classify reviews of businesses as positive or negative based on sentiment analysis of the text.
2. It explores feature selection methods like information gain to identify important features that help determine sentiment. It also discusses using tools like SentiWordNet to assign sentiment scores to words.
3. The proposed system applies a lexical approach using SentiWordNet to quantify word sentiment scores, then uses feature selection and machine learning classifiers like Naive Bayes and Linear SVC to determine the overall sentiment polarity of reviews with over 90% accuracy.
A Intensified Approach On Enhanced Transformer Based Models Using Natural Lan...IRJET Journal
This document discusses and compares two neural network transformer models, BERT and ERNIE, for sentiment analysis. BERT uses bidirectional training of language representations to learn contextual relations between words. ERNIE enhances BERT by integrating knowledge from lexical, syntactic and semantic data during training. The document analyzes how ERNIE uses different masking techniques compared to BERT to better model semantic relationships between words and entities. Experimental results on product review datasets show ERNIE achieves better performance than BERT for sentiment classification tasks.
This document summarizes some useful tips for performing sentiment analysis. It discusses several factors to consider, including:
1) Using both lexicon-based and learning-based techniques, with lexicon-based providing higher precision but lower recall.
2) Considering statistical and syntactic techniques, with statistical techniques being more adaptable to other languages.
3) Training classifiers to detect neutral sentiments in addition to positive and negative, to avoid overfitting.
4) Selecting optimal tokenization, part-of-speech tagging, stemming/lemmatization, and feature selection algorithms for the given topic, language and domain. Feature selection methods like information gain can improve classification accuracy.
With the rapid growth in ecommerce, reviews for popular products on the web have grown rapidly.
Although these reviews are important for making decisions, it is difficult to read all the reviews.
Automating the opinion mining process was identified as a solution for the problem. Although there are
algorithms for opinion mining, an algorithm with better accuracy is needed. A feature and smiley based
algorithm was developed which extracts product features from reviews based on feature frequency and
generates an opinion summary based on product features.
The algorithm was tested on downloaded customer reviews. The sentences were tagged, opinion words
were extracted and opinion orientations were identified using semantic orientation of opinion words and
smileys. Since the precision values for feature extraction and both precision and recall values for opinion
orientation identification were improved by the new algorithm, it is more successful in opinion mining of
customer reviews.
A Critical Evaluation of Popular UX Frameworks Relevant to E-Health Appsrinzindorjej
This research paper aims to evaluate the usability, accessibility, and effectiveness of the current User Experience (UX) frameworks relevant to mobile health apps, with a specific focus on the FitBit app. The selected UX frameworks for evaluation include Nielsen Norman Group's 10 Usability Heuristics, Don Norman's Three Levels of Design, and Human-Centered Design. A mixed-method approach, comprising both qualitative and quantitative analyses, was employed to evaluate these frameworks. The FitBit app was assessed based on the selected frameworks, and the obtained results were compared with existing literature and industry standards. The main subject of this study is the critical evaluation of popular UX frameworks in the context of e-health apps, with a specific emphasis on the FitBit app. The evaluation factors considered include usability, accessibility, and effectiveness. By utilizing the selected UX frameworks, the paper seeks to identify the strengths and limitations of each framework in evaluating e- health apps. The achieved results reveal that Nielsen Norman Group's 10 Usability Heuristics are valuable in identifying usability issues within the FitBit app. Don Norman's Three Levels of Design effectively evaluate the overall user experience, providing insights into the app's design quality. Human- Centered Design, on the other hand, offers a comprehensive and holistic approach to designing for the user, encompassing various aspects of the FitBit app. Through this research, a comprehensive understanding of the strengths and limitations of the evaluated UX frameworks in relation to e-health apps, specifically the FitBit app, is attained. The findings contribute to the existing literature on UX evaluation and provide insights for designers and developers to enhance the user experience of mobile health applications.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy ...journalBEEI
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
More Related Content
Similar to TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING
Co-Extracting Opinions from Online ReviewsEditor IJCATR
Exclusion of opinion targets and words from online reviews is an important and challenging task in opinion mining. The
opinion mining is the use of natural language processing, text analysis and computational process to identify and recover the subjective
information in source materials. This paper propose a Supervised word alignment model, which identifying the opinion relation. Rather
than this paper focused on topical relation, in which to extract the relevant information or features only from a particular online reviews.
It is based on feature extraction algorithm to identify the potential features. Finally the items are ranked based on the frequency of
positive and negative reviews. Compared to previous methods, our model captures opinion relation and feature extraction more precisely.
One of the most advantages that our model obtain better precision because of supervised alignment model. In addition, an opinion
relation graph is used to refer the relationship between opinion targets and opinion words.
IRJET- Cross-Domain Sentiment Encoding through Stochastic Word EmbeddingIRJET Journal
This document discusses cross-domain sentiment encoding through stochastic word embedding. It proposes a novel method that takes advantage of stochastic embedding techniques to tackle cross-domain sentiment alignment in a simple way without complex model designs or additional learning tasks. The method encodes word polarity and occurrence information from reviews to learn representations across domains. It is benchmarked on sentiment classification tasks using two review corpora and compared to other classical and state-of-the-art methods.
A Review on Sentimental Analysis of Application ReviewsIJMER
As with rapid evolution of computer technology and smart phones mobile applications
become very important part of our life. It is very difficult for customers to keep track of different
applications reviews so sentimental analysis is used. Sentimental analysis is effective and efficient
evolution of customer’s opinion in real time. Sentimental analysis for applications review is performed
two approaches statistical model based approaches and Natural Language Processing (NLP) based
approaches to create rules. Two schemes used for analyzing the textual comments- aspect level
sentimental analysis analyses the text and provide a label on each aspect then scores on multiple
aspects are aggregated and result for reviews shown in graphs. Second scheme is document level
analyses which comprising of adjectives, adverbs and verbs and n-gram feature extraction. I have also
used our SentiWordNet scheme to compute the document-level sentiment for each movie reviewed
and compared the results with results obtained using Alchemy API. The sentiment profile of a movie is
also compared with the document-level sentiment result. The results obtained show that my scheme
produces a more accurate and focused sentiment profile than the simple document-level sentiment
analysis.
This document discusses various techniques for sentiment analysis of application reviews, including both statistical and natural language processing approaches. It describes how sentiment analysis can be used to analyze textual reviews and classify them as positive or negative. Several key techniques are discussed, such as using machine learning classifiers like Naive Bayes, extracting n-grams and sentiment-oriented words, and developing rule-based models using techniques like identifying parts of speech. The document also discusses using these techniques to perform sentiment analysis at both the document and aspect levels.
Sentimental analysis of audio based customer reviews without textual conversionIJECEIAES
The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.
MTVRep: A movie and TV show reputation system based on fine-grained sentiment ...IJECEIAES
Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.
Empirical Model of Supervised Learning Approach for Opinion MiningIRJET Journal
This summarizes an empirical model for opinion mining using supervised learning with an integrated alignment model and naive Bayesian classification model. The proposed model aims to automatically identify user reviews of products as positive or negative and provide an aggregated product rating based on review sentiment analysis and rankings. An alignment model is used to match keywords between source and target reviews to determine sentiment polarity. If a match is not found, the review is sent to a naive Bayesian classification model for sentiment analysis and rating. A rank aggregation model then considers data parameters like user ID, time, and rank to generate a ranked list of products based on ratings and sentiment analysis while excluding short-duration sessions or redundant comments. The proposed hybrid model aims to provide more accurate results for product sentiment analysis
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International Journal of Engineering Research and Development (IJERD)IJERD Editor
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This document summarizes a research paper that proposes a method for performing sentiment analysis on product reviews to identify promising product features. It involves scraping short reviews from websites, preprocessing the text through cleaning, tokenization and part-of-speech tagging. Next, it uses pattern mining and a custom lexicon dictionary to determine the overall sentiment score and sentiment scores for specific product features. The goal is to analyze which features consumers view most positively to help businesses understand customer preferences.
Web User Opinion Analysis for Product Features Extraction and Opinion Summari...dannyijwest
Selling the product through Web has become more popular because of online shopping. This enables
merchants to sell their products through Web and expects the customer to express their opinion through
online about the product which they have purchased. Due to this we find number of customer reviews on a
particular product, it varies from hundreds to thousands, for some product it is more than that. In order to
help the customer and the manufacture/merchant we propose a semantic based approach to mine different
product features and to find the opinion summarization about each of these extracted product features by
means of web user opinion expressed through the customer reviews using typed dependency relations.
IRJET- Survey of Classification of Business Reviews using Sentiment AnalysisIRJET Journal
1. The document discusses using machine learning algorithms like Naive Bayes and Linear SVC to classify reviews of businesses as positive or negative based on sentiment analysis of the text.
2. It explores feature selection methods like information gain to identify important features that help determine sentiment. It also discusses using tools like SentiWordNet to assign sentiment scores to words.
3. The proposed system applies a lexical approach using SentiWordNet to quantify word sentiment scores, then uses feature selection and machine learning classifiers like Naive Bayes and Linear SVC to determine the overall sentiment polarity of reviews with over 90% accuracy.
A Intensified Approach On Enhanced Transformer Based Models Using Natural Lan...IRJET Journal
This document discusses and compares two neural network transformer models, BERT and ERNIE, for sentiment analysis. BERT uses bidirectional training of language representations to learn contextual relations between words. ERNIE enhances BERT by integrating knowledge from lexical, syntactic and semantic data during training. The document analyzes how ERNIE uses different masking techniques compared to BERT to better model semantic relationships between words and entities. Experimental results on product review datasets show ERNIE achieves better performance than BERT for sentiment classification tasks.
This document summarizes some useful tips for performing sentiment analysis. It discusses several factors to consider, including:
1) Using both lexicon-based and learning-based techniques, with lexicon-based providing higher precision but lower recall.
2) Considering statistical and syntactic techniques, with statistical techniques being more adaptable to other languages.
3) Training classifiers to detect neutral sentiments in addition to positive and negative, to avoid overfitting.
4) Selecting optimal tokenization, part-of-speech tagging, stemming/lemmatization, and feature selection algorithms for the given topic, language and domain. Feature selection methods like information gain can improve classification accuracy.
With the rapid growth in ecommerce, reviews for popular products on the web have grown rapidly.
Although these reviews are important for making decisions, it is difficult to read all the reviews.
Automating the opinion mining process was identified as a solution for the problem. Although there are
algorithms for opinion mining, an algorithm with better accuracy is needed. A feature and smiley based
algorithm was developed which extracts product features from reviews based on feature frequency and
generates an opinion summary based on product features.
The algorithm was tested on downloaded customer reviews. The sentences were tagged, opinion words
were extracted and opinion orientations were identified using semantic orientation of opinion words and
smileys. Since the precision values for feature extraction and both precision and recall values for opinion
orientation identification were improved by the new algorithm, it is more successful in opinion mining of
customer reviews.
A Critical Evaluation of Popular UX Frameworks Relevant to E-Health Appsrinzindorjej
This research paper aims to evaluate the usability, accessibility, and effectiveness of the current User Experience (UX) frameworks relevant to mobile health apps, with a specific focus on the FitBit app. The selected UX frameworks for evaluation include Nielsen Norman Group's 10 Usability Heuristics, Don Norman's Three Levels of Design, and Human-Centered Design. A mixed-method approach, comprising both qualitative and quantitative analyses, was employed to evaluate these frameworks. The FitBit app was assessed based on the selected frameworks, and the obtained results were compared with existing literature and industry standards. The main subject of this study is the critical evaluation of popular UX frameworks in the context of e-health apps, with a specific emphasis on the FitBit app. The evaluation factors considered include usability, accessibility, and effectiveness. By utilizing the selected UX frameworks, the paper seeks to identify the strengths and limitations of each framework in evaluating e- health apps. The achieved results reveal that Nielsen Norman Group's 10 Usability Heuristics are valuable in identifying usability issues within the FitBit app. Don Norman's Three Levels of Design effectively evaluate the overall user experience, providing insights into the app's design quality. Human- Centered Design, on the other hand, offers a comprehensive and holistic approach to designing for the user, encompassing various aspects of the FitBit app. Through this research, a comprehensive understanding of the strengths and limitations of the evaluated UX frameworks in relation to e-health apps, specifically the FitBit app, is attained. The findings contribute to the existing literature on UX evaluation and provide insights for designers and developers to enhance the user experience of mobile health applications.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy ...journalBEEI
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Similar to TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING (20)
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
- The document presents 6 different models for defining foot size in Tunisia: 2 statistical models, 2 neural network models using unsupervised learning, and 2 models combining neural networks and fuzzy logic.
- The statistical models (SM and SHM) are based on applying statistical equations to morphological foot data.
- The neural network models (MSK and MHSK) use self-organizing Kohonen maps to cluster foot data and model full and half sizes.
- The fuzzy neural network models (MSFK and MHSFK) incorporate fuzzy logic into the neural network learning process to better account for uncertainty in foot sizes.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
DOI: 10.5121/ijcsit.2018.10605 43
TEXT MINING CUSTOMER REVIEWS FOR ASPECT-
BASED RESTAURANT RATING
Jovelyn C. Cuizon , Jesserine Lopez and Danica Rose Jones
University of San Jose-Recoletos, Cebu City, Cebu Philippines
ABSTRACT
This study applies text mining to analyze customer reviews and automatically assign a collective restaurant
star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The
application provides a web and mobile crowd sourcing platform where users share dining experiences and
get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text
reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using
Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the
system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant
star rating is computed based on the individual aspect rating. Further, a word cloud is generated to
provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added
the more reflective the sentiment score to the restaurants’ performance.
KEYWORDS
Text Mining, Sentiment Analysis, Natural Language Processing, Aspect-based scoring
1. INTRODUCTION
Customer feedbacks are useful for firms in order for them to recognize its strengths and
weaknesses, and therefore generate ideas to improve its services. The proliferation of a wide
variety of communication media has provided customers the capability to write and express their
experiences about the products and services availed. Crowdsourcing feedback gives the
customers the power to influence prospective customer’s decision to avail of the products and
services offered. Crowdsourcing applications have gained a lot of attention because it harnesses
the potential of diverse group of people to provide information through various media. Zomato
and Yelp are some of the many available crowdsourcing applications that gather customer
feedback on restaurants. However, customer reviews come in bulk of unstructured text data that
people need to read to understand the general perception of the customer on a restaurant.
Customers are usually asked to assign a star rating in the range of 1 to 5 to assess the overall
experience which may not necessary reflect the opinion in the textual feedback. The application
of text mining for analysis of customer textual reviews to quantify it through star rating based on
predetermined decision factors prove to be beneficial to help cope with the information overload
and facilitate decision making.
Text mining encompasses varied techniques to analyze and digest information from textual data
including natural language processing, information retrieval, data mining and machine learning
[1]. Customer reviews served as corpus to understand the general perception of the customer
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
44
towards the products and services. A study by Jack and Tsai relied on term frequency (n-gram) to
aggregate top attributes and issues associated with devices such as laptops and tablets expressed
in customer reviews to understand what customers liked or disliked about the products. [2]
Ordenes et.al applied linguistic-based approach to evaluate the value creation components in
customer feedback which influences customer experience [3]. Suresh et. al applied aspect-based
opinion mining to recommend related restaurant reviews filtered according to predetermined
features [4]. Hu et. al [5] and Somprasertsri et. al [6] applied text summarization to mine product
features and opinions.
While there has been significant amount of study on text mining and sentiment analysis to
understand customer reviews, converting textual data to numeric assessment to reflect overall
perception of customer has not been extensively explored. System-assigned star rating minimizes
if not eliminates inconsistency in the opinion expressed in text and the user-assigned numeric
assessment.
This study aims to develop a mobile and web application that serves as a platform for diners to
write feedback on dining experience. The system uses these reviews as corpus to determine
customer perception on the restaurant in general and on the specific aspects such as ambience,
cost, food, hygiene and service. A word cloud of the customers’ general sentiment will give the
restaurant the visual illustration of top qualities and issues.
2. RELATED WORKS
In order to understand the underlying meaning of a given text, text analysis algorithms are applied
which enabled users to rapidly transform the key content in text documents into quantitative,
actionable insights. Text mining encompasses techniques in data mining, information retrieval
and natural language processing.
There have been a generous amount of studies on text mining for analysis of customer feedback
or reviews. The study conducted by [7] and [8] incorporates the use of natural language
processing to extract noun and adjective pairs from sentences through Parts-of-speech (POS)
tagging and association rule mining on customer reviews of products to find frequent and
infrequent features to ascertain product characteristics. Another study by [9] performed
sentiment analysis and linguistic rules to analyze reviews and detect opinion orientation and
important aspects about a restaurant.
A model proposed by [10] called Multi-Aspect Sentiment (MAS) model to discover topics in
customer reviews and extract fragments of text that correspond to rateable aspect to support
numerical ratings. An unsupervised method proposed by [11] extracts important aspects of a
product to estimate an aspect rating from 1 to 5 to represent overall customer satisfaction.
3. PROPOSED WORK
As shown in Figure 1, the application runs in mobile and web platforms. The mobile component
is developed using Android. The web application is developed using Java Platform, Enterprise
Edition (J2EE) with Maven as a build automation tool and utilizes Spring for MVC (Model-
View-Controller) framework for implementation. An application server is implemented which
manages process requests from and to the mobile and web client applications through RESTful
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol
web service calls. To ensure availability of data, Google app engine is used as cloud data store
and for push notification services.
A number of libraries and APIs are used to support in the development. Stanford Core NLP API
is used to perform parts-of-speech (POS) tagging and noun
AFINN library is utilized for determining polarity of words. Google Maps API, an Application
Programming Interface is used to embed Google Maps.
3.1 Noun-Adjective Pairs Extraction
Automatic assignment of feature
natural language processing (NLP) techniques on text reviews. Customer reviews can range from
one phrase to sentences to paragraphs. A sample of a review is as follows:
“This is one of the best places to eat lechon. The place is clean, the staff is friendly, and they have
a menu that is filled with dishes that go so well with Cebu’s Lechon. I usually take out of towners
here when they crave for lechon and so far, all of them were happy with this pl
favorites here is their Carcar Lechon.”
The text goes through sentence segmentation as shown in Figure 2 which generates a list of text
sentences, one in which that begins with capital letter and ends with a boundary punctuation
marks. Sentence boundary punctuations included the period, question mark and exclamation
point.
Figure 2 List of sentences after segmentation
Extracted sentences are passed to the Stanford CoreNLP API to retrieve a tree of dependencies in
the sentence which is used to extract the noun
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
web service calls. To ensure availability of data, Google app engine is used as cloud data store
otification services.
Figure 1 Architectural Diagram
A number of libraries and APIs are used to support in the development. Stanford Core NLP API
speech (POS) tagging and noun-adjective pair extraction in sentences.
AFINN library is utilized for determining polarity of words. Google Maps API, an Application
Programming Interface is used to embed Google Maps.
djective Pairs Extraction
Automatic assignment of feature assessment rating is performed through the application of
natural language processing (NLP) techniques on text reviews. Customer reviews can range from
one phrase to sentences to paragraphs. A sample of a review is as follows:
aces to eat lechon. The place is clean, the staff is friendly, and they have
a menu that is filled with dishes that go so well with Cebu’s Lechon. I usually take out of towners
here when they crave for lechon and so far, all of them were happy with this place. Among my
favorites here is their Carcar Lechon.”
The text goes through sentence segmentation as shown in Figure 2 which generates a list of text
sentences, one in which that begins with capital letter and ends with a boundary punctuation
Sentence boundary punctuations included the period, question mark and exclamation
Figure 2 List of sentences after segmentation
Extracted sentences are passed to the Stanford CoreNLP API to retrieve a tree of dependencies in
s used to extract the noun-adjective (NA) pairs in the sentence.
10, No 6, December 2018
45
web service calls. To ensure availability of data, Google app engine is used as cloud data store
A number of libraries and APIs are used to support in the development. Stanford Core NLP API
e pair extraction in sentences.
AFINN library is utilized for determining polarity of words. Google Maps API, an Application
assessment rating is performed through the application of
natural language processing (NLP) techniques on text reviews. Customer reviews can range from
aces to eat lechon. The place is clean, the staff is friendly, and they have
a menu that is filled with dishes that go so well with Cebu’s Lechon. I usually take out of towners
ace. Among my
The text goes through sentence segmentation as shown in Figure 2 which generates a list of text-
sentences, one in which that begins with capital letter and ends with a boundary punctuation
Sentence boundary punctuations included the period, question mark and exclamation
Extracted sentences are passed to the Stanford CoreNLP API to retrieve a tree of dependencies in
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
46
Figure 3 Dependency tree of sentences
The adjectival modifier of a noun phrase (NP) (amod) and the nominal subject (nsubj)
dependencies were extracted from the dependency tree. The list of NA pairs in each sentence is
appended to a global list of NA pairs for the review. Likewise, parts-of-speech (POS) tagging is
performed using Stanford POS Tagger to tag each word according to its part of speech. This will
be used to determine the degree of the adjective (positive, comparative, superlative) to give
appropriate sentiment weight. Table 1 shows example of POS tags for adjectives in different
degrees.
Table 1. Sample POS Tags
POS Tag Description Example
JJ adjective big
JJR adjective, comparative bigger
JJS adjective, superlative biggest
3.2 Scoring Algorithm
After getting the list of NA pairs, the system attributes all nouns into five predetermined
categories namely: ambiance, cost, food, hygiene, and service by checking its occurrence on a
bag-of-words associated with each category.
All noun-adjective pairs which were attributed to the five predetermined categories were
processed for polarity. The AFINN lexicon which assigns sentiment weights to NA pairs in the
range of -5 to +5. For NA pairs which are not in the AFINN lexicon will be given a default
sentiment weight of 1.
Further, a multiplier score is assigned for the degree of adjective, JJ = 1; JJR = 3; JJS = 5. Sample
adjectives that match with the tags are enumerated in the form “adjective(POS tag, score)” as
follows: bad(JJ:1), best(JJS:5), fresh(JJ:1), great(JJS:5), negative(JJ:1), new(JJ:1), strange(JJ:1).
The final weight of the NA pair is computed as the product of the sentiment weight (sw) based on
the AFINN lexicon and the multiplier score representing the degree of the adjective (deg). The
rating per category for one review is computed as:
( ) =
∑ ( ∗ )
(1)
where n is the count of distinct reviews from customers and rating(X) is the computed aspect
rating per individual review.
( ) =
∑ ( )
(2)
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47
The average of the overall aspect rating represents the restaurant star rating. The rating adjusts as new
reviews are added. The resulting rating computation would yield a number in the range [-15, +15]. In order
to convert computed score to a number in the range [1,5] representing star rating, the affine transformation
is applied. Affine transformation maintains the property of order and the relative distances between scale
values [8] using the following formula:
= ( − )
"#
$"
+ & (3)
where x is the original computed value in the range [a,b] and y is the transformed value in the
range [c,d]. The transformed score is used to assign the overall restaurant star rating. The overall
aspect rating is the average of the individual review aspect rating. Figure 4 shows the overall
computational process.
Figure 4. Conceptual Diagram
3.3 Word Cloud Generation
A word cloud is a visual representation of frequently used terms in a collection of text. The
collective customer reviews made for each restaurant will serve as the text corpus.
Unstructured text underwent data pre-processing such as removal of stop words, punctuation
marks and numbers and extra white spaces and converted to lowercase for uniformity in the
presentation. A document term matrix was derived the pre-processing stage which is a list of
distinct words with its corresponding frequency count for each review as shown in Table 2.
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol
Table 2. Frequently occurring words in the review
Word
lechon
good
cebu
place
spicy
best
food
one
taste
like
R libraries wordcloud2 and tm were used to generate the word cloud. Rserve facilitates
the execution of R scripts and writes the image file of the word cloud into the web server
home directory. The word cloud is updated each time a new review is added. Fig
shows a sample of a word cloud. The size and the thickness of the word appearing in the
image reflect the most frequently occurring descriptor of the restaurant.
4. RESULTS AND DISCUSSI
The system is weakly supervised using associated words fed to the system. Table 3 shows the list
of associated words for each aspect with a total count of 2362.
Table 3. Associated words for each aspect
Aspect
ambience atmosphere, vibe, mood, surroundings…
cost price, expense, fee, value, pay, budget…
food
hygiene
service
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
Table 2. Frequently occurring words in the review
Frequency
122
38
34
25
20
19
18
17
15
14
R libraries wordcloud2 and tm were used to generate the word cloud. Rserve facilitates
the execution of R scripts and writes the image file of the word cloud into the web server
home directory. The word cloud is updated each time a new review is added. Fig
shows a sample of a word cloud. The size and the thickness of the word appearing in the
image reflect the most frequently occurring descriptor of the restaurant.
Figure 5 Generated wordcloud
RESULTS AND DISCUSSIONS
The system is weakly supervised using associated words fed to the system. Table 3 shows the list
of associated words for each aspect with a total count of 2362.
Table 3. Associated words for each aspect
Associated words Word count
atmosphere, vibe, mood, surroundings… 168
price, expense, fee, value, pay, budget… 590
meal, gourmet, rice, chicken, pork.. 640
clean, sanitation, tidiness, disposal,.. 283
delivery, care, assistance, employee .. 681
10, No 6, December 2018
48
R libraries wordcloud2 and tm were used to generate the word cloud. Rserve facilitates
the execution of R scripts and writes the image file of the word cloud into the web server
home directory. The word cloud is updated each time a new review is added. Figure 5
shows a sample of a word cloud. The size and the thickness of the word appearing in the
The system is weakly supervised using associated words fed to the system. Table 3 shows the list
Word count
168
590
640
283
681
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol
One of the limitations of the application is that it can only assess text reviews written in the
English language. Further, the system made use of the lexicon
AFINN. The AFINN lexicon is limited only to 2477 words and therefore may n
assign scores to noun-adjective pairs which are not included in the library.
4.1 User Interface
Web and mobile client applications were developed to provide an interface for the user to
contribute reviews on dining experience (food trip) on
food trip details including restaurant name, menu ordered, and text review of the experience.
Figure 6 shows the user interface used to gather customer feedback through crowdsourcing.
Data contributed by users are processed to automatically assign rating to each pre
categories. Each user review contributes to the overall rating of the restaurant and the specific
categories evaluated such as ambiance, cos
screenshot of the restaurant profile page which contains system
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
the limitations of the application is that it can only assess text reviews written in the
English language. Further, the system made use of the lexicon-based sentiment scoring using
AFINN. The AFINN lexicon is limited only to 2477 words and therefore may n
adjective pairs which are not included in the library.
Web and mobile client applications were developed to provide an interface for the user to
contribute reviews on dining experience (food trip) on a certain restaurant. Customer specifies
food trip details including restaurant name, menu ordered, and text review of the experience.
shows the user interface used to gather customer feedback through crowdsourcing.
Figure 6. Foodtrip Entry Form
Data contributed by users are processed to automatically assign rating to each pre
categories. Each user review contributes to the overall rating of the restaurant and the specific
categories evaluated such as ambiance, cost, food, hygiene and service. Figure
screenshot of the restaurant profile page which contains system-generated rating.
10, No 6, December 2018
49
the limitations of the application is that it can only assess text reviews written in the
based sentiment scoring using
AFINN. The AFINN lexicon is limited only to 2477 words and therefore may not be able to
Web and mobile client applications were developed to provide an interface for the user to
a certain restaurant. Customer specifies
food trip details including restaurant name, menu ordered, and text review of the experience.
shows the user interface used to gather customer feedback through crowdsourcing.
Data contributed by users are processed to automatically assign rating to each pre-identified
categories. Each user review contributes to the overall rating of the restaurant and the specific
t, food, hygiene and service. Figure 7 shows a
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
50
Figure 7 Customer Review with Feature Ratings
4.2 Accuracy Testing
The respondents were made to rate each aspect (ambiance, cost, food, hygiene, service) based on
a given text reviews which are similar to the ones fed to the system. The experimental value is the
aspect rating assigned by the system while theoretical value is the rating assigned through human
interpretation. Figure 8 depicts the difference in human and the system generated aspect rating
when tested with five (5) distinct text reviews.
Figure 8. Aspect rating for using human interpretation and system result
In order to quantify the degree of closeness between the system-assigned feature rating and the
manually assigned rating based on a given text review, experimental error is derived. The percent
error is the ratio of the error to the actual value multiplied by 100. Table 4 shows mean percent
error result for each aspect.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Human
Interpretation
System Result
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
51
percent error = | experimental value – theoretical value | x 100 (4)
theoretical value
Table 4. Mean percent error per aspect
Aspect Human
Interpretation
System
Result
%error Point
Difference
Ambiance 4.0 3.69 7.75 0.39
Cost 3.2 3.08 3.75 0.19
Food 3.6 3.93 9.16 0.46
Hygiene 3.8 3.27 13.94 0.70
Service 3.8 3.37 11.32 0.57
The system generated some difference in the ratings on each aspect compared to the manually
assigned rating. The difference may be influenced by the fact that the human evaluator used
whole number values from 1 to 5 to assess while the system generated rating can result to a real
number. Cost is the aspect which yields the least percent error because associated words related to
cost are unambiguous and plenty which may not be true for hygiene. The point difference in all
aspects is lesser than 1 which reflects that the system generated result is generally acceptable.
5. CONCLUSIONS
The development of the application is intended to make use of unstructured text to extract
relevant descriptors and assessment ratings for user-identified features through natural
language processing (NLP). The system could be built on top of existing customer review
platform to provide automatic rates of predefined aspects of products and services
evaluated.
REFERENCES
[1] C. C. Aggarwal and C. Zhai, Mining Text Data, Springer Science & Business Media, 2012.
[2] L. Jack and Y. Tsai, "Using Text Mining of Amazon Reviews to Explore," in The 2015 International
Conference on Data Mining, Las Vegas, Nevada, USA, 2015.
[3] F. V. Ordenes, ,. B. Theodoulidis, J. Burton, T. Gruber and M. Zaki, "Analyzing Customer
Experience Feedback Using Text Mining A Linguistics-Based Approach," Journal of Service
Research, vol. 17, no. 4, 2014.
[4] V. Suresh, S. Roohi and M. Eirinaki, "Aspect-Based Opinion Mining and Recommendation System
for Restaurant Reviews," in ACM RecSys'14, 2014.
[5] M. Hu and B. Liu, "Mining Opinion Features in Customer Reviews," AAAI, vol. 4, no. 4, 2004.
[6] G. Somprasertsri and P. Lalitrojwong, "Mining Feature-Opinion in Online Customer Reviews for,"
Journal of Universal Computer Science,, vol. 16, no. 6, pp. 938-955, 2010.
[7] M. Hu and B. Liu, Mining and Summarizing Customer Reviews, in KDD ’04 : Proceedings of the
tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–
177, New York, NY, USA, 2004, ACM.
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[8] A. Gupta, T. Tenneti and A. Gupta. Sentiment based Summarization of Restaurant Reviews, June 3,
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[9] Chinsa T C, Shibily J. Aspect based Opinion Mining from Restaurant Reviews, International Journal
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[10] Titov, Ivan, and Ryan McDonald. "A joint model of text and aspect ratings for sentiment
summarization." proceedings of ACL-08: HLT,308-316, 2008
[11] Samaneh Moghaddam and Martin Ester.. Opinion digger: an unsupervised opinion miner from
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DOI=http://dx.doi.org/10.1145/1871437.1871739
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Authors
Jovelyn Cuizon is an assistant professor at University of San Jose- Recoletos. She is the academic head for
the Computer Science department of the same university. She graduated Master of Science in Information
Technology and Doctor in Management in 2004 and 2018 respectively.
Jesserine Lopez graduated in 2017 with Bachelor of Science in Computer Science from University of San
Jose-Recoletos, Cebu City, Philippines. She is currently a software engineer at Accenture.
Danica Rose Jones graduated in 2017 with Bachelor of Science in Computer Science from University of
San Jose-Recoletos, Cebu City, Philippines. She is currently a software engineer at Advanced World
Systems.