International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses opinion mining and sentiment analysis. It begins with introductions to sentiment, opinion mining, and the motivation for opinion mining including analyzing large amounts of opinionated online text. It then discusses challenges in opinion mining including distinguishing subjects and targets. It describes classifying sentiment at the word, sentence and document levels. Applications mentioned include information extraction, product reviews, and tracking sentiments. The document provides an overview of key concepts in opinion mining and sentiment analysis.
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Tips for Scale Development: Evaluating Automatic PersonasJoni Salminen
This document discusses research on automatically generating persona profiles from online data. It describes an Automatic Persona Generation (APG) system that aims to computationally analyze vast amounts of online data to discover useful representations of personas. Various techniques are discussed for different aspects of persona generation, including information architecture, commenting analysis, profile picture generation, topic classification, and temporal analysis of how personas change over time. It also discusses challenges in evaluating generated personas, both in terms of objective accuracy and subjective user perceptions. The document provides tips and guidelines for developing a persona perception scale to systematically measure how users view automatically generated personas.
Sentiment analysis and opinion mining is almost same thing however there is minor difference between them that is opinion mining extracts and analyze people's opinion about an entity while Sentiment analysis search for the sentiment words/expression in a text and then analyze it.
It uses machine learning techniques like SVM (Support Vector Machines) to analyze the text and classify them as positive, negative or neutral.
Sentiment analysis is the computational study of opinions, attitudes, and emotions toward entities. There are three main classification levels: document, sentence, and aspect. Data used can include product reviews, stock markets, news articles, and political debates. Key steps involve feature selection like terms, parts of speech, opinion words, and negations. Common techniques are machine learning algorithms like supervised and unsupervised learning, as well as lexicon-based approaches using dictionaries or analyzing corpora. The techniques aim to determine sentiment at the document or aspect level.
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
This is seminar report on Sentiment Analysis.This report gives the brief introduction to what is sentiment analysis?what are the various ways to implement it?
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
This document discusses opinion mining and sentiment analysis. It begins with introductions to sentiment, opinion mining, and the motivation for opinion mining including analyzing large amounts of opinionated online text. It then discusses challenges in opinion mining including distinguishing subjects and targets. It describes classifying sentiment at the word, sentence and document levels. Applications mentioned include information extraction, product reviews, and tracking sentiments. The document provides an overview of key concepts in opinion mining and sentiment analysis.
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Tips for Scale Development: Evaluating Automatic PersonasJoni Salminen
This document discusses research on automatically generating persona profiles from online data. It describes an Automatic Persona Generation (APG) system that aims to computationally analyze vast amounts of online data to discover useful representations of personas. Various techniques are discussed for different aspects of persona generation, including information architecture, commenting analysis, profile picture generation, topic classification, and temporal analysis of how personas change over time. It also discusses challenges in evaluating generated personas, both in terms of objective accuracy and subjective user perceptions. The document provides tips and guidelines for developing a persona perception scale to systematically measure how users view automatically generated personas.
Sentiment analysis and opinion mining is almost same thing however there is minor difference between them that is opinion mining extracts and analyze people's opinion about an entity while Sentiment analysis search for the sentiment words/expression in a text and then analyze it.
It uses machine learning techniques like SVM (Support Vector Machines) to analyze the text and classify them as positive, negative or neutral.
Sentiment analysis is the computational study of opinions, attitudes, and emotions toward entities. There are three main classification levels: document, sentence, and aspect. Data used can include product reviews, stock markets, news articles, and political debates. Key steps involve feature selection like terms, parts of speech, opinion words, and negations. Common techniques are machine learning algorithms like supervised and unsupervised learning, as well as lexicon-based approaches using dictionaries or analyzing corpora. The techniques aim to determine sentiment at the document or aspect level.
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
This is seminar report on Sentiment Analysis.This report gives the brief introduction to what is sentiment analysis?what are the various ways to implement it?
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
The document describes research aimed at creating a gold standard for evaluating automated detection of reflection in text. It outlines related work on quantifying reflection which used various scales and measures reflection at the study or student level. The researchers developed a model of reflection with 7 dimensions and applied a crowdsourcing methodology to rate 1000 sentences from an Open University forum. Interrater reliability increased from 0.22 to 0.36 and 0.581 when requiring higher levels of rater agreement (3 or 4 of 5 raters) indicating the crowdsourced ratings can provide a gold standard for evaluating automated reflection detection.
Exploratory research is initial, informal research conducted to clarify and define problems. It often involves qualitative data from techniques like interviews, case studies, and pilot studies. The goal is to gain insights rather than make conclusive findings. This chapter discusses common exploratory research methods like experience surveys, secondary data analysis, focus groups, projective techniques, and in-depth interviews. It explains how each technique works and its advantages/disadvantages. The purpose of exploratory research is to better understand a situation and generate ideas before quantitative studies are conducted.
Nowadays peoples are actively involved in giving comments and reviews on social networking websites
and other websites like shopping websites, news websites etc. large number of people everyday share
their opinion on the web, results is a large number of user data is collected .users also find it trivial task
to read all the reviews and then reached into the decision. It would be better if these reviews are
classified into some category so that the user finds it easier to read. Opinion Mining or Sentiment
Analysis is a natural language processing task that mines information from various text forms such as
reviews, news, and blogs and classify them on the basis of their polarity as positive, negative or neutral.
But, from the last few years, user content in Hindi language is also increasing at a rapid rate on the Web.
So it is very important to perform opinion mining in Hindi language as well. In this paper a Hindi
language opinion mining system is proposed. The system classifies the reviews as positive, negative and
neutral for Hindi language. Negation is also handled in the proposed system. Experimental results using
reviews of movies show the effectiveness of the system.
Comparing Automatically Detected Reflective Texts with Human JudgementsThomas Ullmann
Slides from my presentation at the Awareness and Reflection in Technology-Enhanced Learning Workshop at the EC-TEL 2012 Conference. For more information about the workshop and the presentation please visit http://teleurope.eu/artel12.
An Architecture for the Automated Detection of Textual Indicators of ReflectionThomas Ullmann
Presented at the 1st European Workshop on Awareness and Reflection in Learning Networks. In conjunction with the EC-TEL 2011 conferece, Palermo, Italy.
Proceedings online at: http://ceur-ws.org/Vol-790/
A Survey on Sentiment Mining TechniquesKhan Mostafa
The document summarizes a survey paper on sentiment mining techniques. It discusses 7 papers that address different aspects of sentiment analysis, including identifying sentiment from text, classifying sentiment polarity, using Twitter data for analysis, incorporating topics with sentiment, handling streaming data, and addressing irony. The papers cover techniques like machine learning classifiers, sentiment lexicons, topic models, and evaluating algorithms on real-world data streams. The survey concludes that each paper provides insights into building complete solutions for large-scale sentiment analysis.
Opinion mining in hindi language a surveyijfcstjournal
Opinions are very important in the life of human beings. These Opinions helped the humans to carry out
the decisions. As the impact of the Web is increasing day by day, Web documents can be seen as a new
source of opinion for human beings. Web contains a huge amount of information generated by the users
through blogs, forum entries, and social networking websites and so on To analyze this large amount of
information it is required to develop a method that automatically classifies the information available on the
Web. This domain is called Sentiment Analysis and Opinion Mining. Opinion Mining or Sentiment Analysis
is a natural language processing task that mine information from various text forms such as reviews, news,
and blogs and classify them on the basis of their polarity as positive, negative or neutral. But, from the last
few years, enormous increase has been seen in Hindi language on the Web. Research in opinion mining
mostly carried out in English language but it is very important to perform the opinion mining in Hindi
language also as large amount of information in Hindi is also available on the Web. This paper gives an
overview of the work that has been done Hindi language.
This document discusses machine learning approaches for sentiment analysis. It begins by defining sentiment analysis as identifying the orientation of opinions in text through predicting the attitude, opinions, and emotions. The objective is to determine a writer's attitude on a given topic by analyzing text at the document, sentence, and phrase level. Feature selection methods and sentiment classification techniques are discussed, including lexicon-based approaches using dictionaries and corpora, and machine learning approaches using supervised and unsupervised learning with classifiers like naive Bayes and SVMs. Deep learning models for sentiment analysis including CNNs, RNNs, and LSTMs are also covered. The document concludes by discussing applications and potential future work exploring the cognitive aspects of sentiment analysis.
Quantitative research involves collecting numerical data and analyzing it using statistical methods. It is well-suited for answering questions that require quantitative answers, measuring numerical change over time, explaining phenomena through predictive relationships, and testing hypotheses about potential causal relationships between variables. While quantitative research provides breadth of information from many units, qualitative research is better for exploring issues in greater depth through methods like interviews and case studies.
The introduction discusses altruism from multiple perspectives such as genetics and evolution. It defines altruism and provides background on studies of altruism. The essay aims to examine altruism from an evolutionary perspective and identify three motivating factors for altruism that will be evaluated.
Ms 95 - research methodology for management decisionssmumbahelp
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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This document provides guidance on conceptual and theoretical frameworks, literature reviews, hypotheses, and definitions of terms for research. It discusses that frameworks provide perspectives and models used by researchers to organize their work. Theoretical frameworks apply theories to explain phenomena, while conceptual frameworks graphically present concepts and relationships. Literature reviews help establish connections to prior work, theories, and accuracy of research questions. Hypotheses tentatively explain variable relationships and must be testable. Definitions of terms clarify meanings within a study. Organization and criteria are outlined for effective literature reviews.
This document discusses qualitative research methods. It begins by distinguishing between methods and methodology, with methods being the specific tools used to collect data and methodology referring to the overall research philosophy and approach. Some common qualitative methods are discussed, including interviews, focus groups, and participant observation. The document then covers key aspects of qualitative methodology, such as philosophical underpinnings regarding ontology, epistemology and axiology. Examples of coding and thematic analysis are provided as approaches to analyzing qualitative data. Thematic analysis involves identifying common themes across a data set through a six step process of familiarization, coding, generating themes, reviewing themes, defining themes, and writing up the analysis.
The document discusses different types of interviews and surveys that can be used for research purposes. It describes unstructured, semi-structured, and structured interviews. It also outlines Kvale's seven stages of interviews which include designing, conducting, transcribing, analyzing, verifying, and reporting. Additional tips provided include establishing rapport with interviewees, deciding how to record the interview, analyzing interview texts, and frequently asked questions about interviews. The document also discusses open-ended and closed-ended questionnaires and considerations for designing surveys.
This document presents a summary of sentiment analysis techniques for classifying tweets as having positive or negative sentiment. It discusses representing text as bag-of-words vectors and using a Naive Bayes classifier trained on labeled tweets. The techniques covered include preprocessing text, removing stop words, stemming, constructing word n-grams, and building word frequency vectors. The document concludes that a Naive Bayes approach using pre-trained word vectors achieves good performance for Twitter sentiment analysis.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este decreto autoriza al Ejecutivo Nacional a suprimir y liquidar el Instituto Venezolano de los Seguros Sociales (IVSS) y establece el proceso de transición al nuevo sistema de seguridad social integral. Se designará una Junta Liquidadora para administrar los bienes y derechos del IVSS, transferir su infraestructura física y cumplir con sus obligaciones, hasta completar la liquidación antes del 31 de diciembre de 1999. El Ministerio del Trabajo supervisará la Junta Liquidadora y el proceso de liquidación del IVSS.
O documento apresenta vários gráficos estatísticos com resultados de pesquisas sobre tópicos como: origem geográfica dos usuários de serviços online, dispositivos utilizados para acesso, faixas de receita de empresas e adoção de certificações e metodologias. As tabelas e gráficos fornecem dados percentuais sobre essas variáveis, com comparações entre regiões e portes de empresas.
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
The document describes research aimed at creating a gold standard for evaluating automated detection of reflection in text. It outlines related work on quantifying reflection which used various scales and measures reflection at the study or student level. The researchers developed a model of reflection with 7 dimensions and applied a crowdsourcing methodology to rate 1000 sentences from an Open University forum. Interrater reliability increased from 0.22 to 0.36 and 0.581 when requiring higher levels of rater agreement (3 or 4 of 5 raters) indicating the crowdsourced ratings can provide a gold standard for evaluating automated reflection detection.
Exploratory research is initial, informal research conducted to clarify and define problems. It often involves qualitative data from techniques like interviews, case studies, and pilot studies. The goal is to gain insights rather than make conclusive findings. This chapter discusses common exploratory research methods like experience surveys, secondary data analysis, focus groups, projective techniques, and in-depth interviews. It explains how each technique works and its advantages/disadvantages. The purpose of exploratory research is to better understand a situation and generate ideas before quantitative studies are conducted.
Nowadays peoples are actively involved in giving comments and reviews on social networking websites
and other websites like shopping websites, news websites etc. large number of people everyday share
their opinion on the web, results is a large number of user data is collected .users also find it trivial task
to read all the reviews and then reached into the decision. It would be better if these reviews are
classified into some category so that the user finds it easier to read. Opinion Mining or Sentiment
Analysis is a natural language processing task that mines information from various text forms such as
reviews, news, and blogs and classify them on the basis of their polarity as positive, negative or neutral.
But, from the last few years, user content in Hindi language is also increasing at a rapid rate on the Web.
So it is very important to perform opinion mining in Hindi language as well. In this paper a Hindi
language opinion mining system is proposed. The system classifies the reviews as positive, negative and
neutral for Hindi language. Negation is also handled in the proposed system. Experimental results using
reviews of movies show the effectiveness of the system.
Comparing Automatically Detected Reflective Texts with Human JudgementsThomas Ullmann
Slides from my presentation at the Awareness and Reflection in Technology-Enhanced Learning Workshop at the EC-TEL 2012 Conference. For more information about the workshop and the presentation please visit http://teleurope.eu/artel12.
An Architecture for the Automated Detection of Textual Indicators of ReflectionThomas Ullmann
Presented at the 1st European Workshop on Awareness and Reflection in Learning Networks. In conjunction with the EC-TEL 2011 conferece, Palermo, Italy.
Proceedings online at: http://ceur-ws.org/Vol-790/
A Survey on Sentiment Mining TechniquesKhan Mostafa
The document summarizes a survey paper on sentiment mining techniques. It discusses 7 papers that address different aspects of sentiment analysis, including identifying sentiment from text, classifying sentiment polarity, using Twitter data for analysis, incorporating topics with sentiment, handling streaming data, and addressing irony. The papers cover techniques like machine learning classifiers, sentiment lexicons, topic models, and evaluating algorithms on real-world data streams. The survey concludes that each paper provides insights into building complete solutions for large-scale sentiment analysis.
Opinion mining in hindi language a surveyijfcstjournal
Opinions are very important in the life of human beings. These Opinions helped the humans to carry out
the decisions. As the impact of the Web is increasing day by day, Web documents can be seen as a new
source of opinion for human beings. Web contains a huge amount of information generated by the users
through blogs, forum entries, and social networking websites and so on To analyze this large amount of
information it is required to develop a method that automatically classifies the information available on the
Web. This domain is called Sentiment Analysis and Opinion Mining. Opinion Mining or Sentiment Analysis
is a natural language processing task that mine information from various text forms such as reviews, news,
and blogs and classify them on the basis of their polarity as positive, negative or neutral. But, from the last
few years, enormous increase has been seen in Hindi language on the Web. Research in opinion mining
mostly carried out in English language but it is very important to perform the opinion mining in Hindi
language also as large amount of information in Hindi is also available on the Web. This paper gives an
overview of the work that has been done Hindi language.
This document discusses machine learning approaches for sentiment analysis. It begins by defining sentiment analysis as identifying the orientation of opinions in text through predicting the attitude, opinions, and emotions. The objective is to determine a writer's attitude on a given topic by analyzing text at the document, sentence, and phrase level. Feature selection methods and sentiment classification techniques are discussed, including lexicon-based approaches using dictionaries and corpora, and machine learning approaches using supervised and unsupervised learning with classifiers like naive Bayes and SVMs. Deep learning models for sentiment analysis including CNNs, RNNs, and LSTMs are also covered. The document concludes by discussing applications and potential future work exploring the cognitive aspects of sentiment analysis.
Quantitative research involves collecting numerical data and analyzing it using statistical methods. It is well-suited for answering questions that require quantitative answers, measuring numerical change over time, explaining phenomena through predictive relationships, and testing hypotheses about potential causal relationships between variables. While quantitative research provides breadth of information from many units, qualitative research is better for exploring issues in greater depth through methods like interviews and case studies.
The introduction discusses altruism from multiple perspectives such as genetics and evolution. It defines altruism and provides background on studies of altruism. The essay aims to examine altruism from an evolutionary perspective and identify three motivating factors for altruism that will be evaluated.
Ms 95 - research methodology for management decisionssmumbahelp
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides guidance on conceptual and theoretical frameworks, literature reviews, hypotheses, and definitions of terms for research. It discusses that frameworks provide perspectives and models used by researchers to organize their work. Theoretical frameworks apply theories to explain phenomena, while conceptual frameworks graphically present concepts and relationships. Literature reviews help establish connections to prior work, theories, and accuracy of research questions. Hypotheses tentatively explain variable relationships and must be testable. Definitions of terms clarify meanings within a study. Organization and criteria are outlined for effective literature reviews.
This document discusses qualitative research methods. It begins by distinguishing between methods and methodology, with methods being the specific tools used to collect data and methodology referring to the overall research philosophy and approach. Some common qualitative methods are discussed, including interviews, focus groups, and participant observation. The document then covers key aspects of qualitative methodology, such as philosophical underpinnings regarding ontology, epistemology and axiology. Examples of coding and thematic analysis are provided as approaches to analyzing qualitative data. Thematic analysis involves identifying common themes across a data set through a six step process of familiarization, coding, generating themes, reviewing themes, defining themes, and writing up the analysis.
The document discusses different types of interviews and surveys that can be used for research purposes. It describes unstructured, semi-structured, and structured interviews. It also outlines Kvale's seven stages of interviews which include designing, conducting, transcribing, analyzing, verifying, and reporting. Additional tips provided include establishing rapport with interviewees, deciding how to record the interview, analyzing interview texts, and frequently asked questions about interviews. The document also discusses open-ended and closed-ended questionnaires and considerations for designing surveys.
This document presents a summary of sentiment analysis techniques for classifying tweets as having positive or negative sentiment. It discusses representing text as bag-of-words vectors and using a Naive Bayes classifier trained on labeled tweets. The techniques covered include preprocessing text, removing stop words, stemming, constructing word n-grams, and building word frequency vectors. The document concludes that a Naive Bayes approach using pre-trained word vectors achieves good performance for Twitter sentiment analysis.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este decreto autoriza al Ejecutivo Nacional a suprimir y liquidar el Instituto Venezolano de los Seguros Sociales (IVSS) y establece el proceso de transición al nuevo sistema de seguridad social integral. Se designará una Junta Liquidadora para administrar los bienes y derechos del IVSS, transferir su infraestructura física y cumplir con sus obligaciones, hasta completar la liquidación antes del 31 de diciembre de 1999. El Ministerio del Trabajo supervisará la Junta Liquidadora y el proceso de liquidación del IVSS.
O documento apresenta vários gráficos estatísticos com resultados de pesquisas sobre tópicos como: origem geográfica dos usuários de serviços online, dispositivos utilizados para acesso, faixas de receita de empresas e adoção de certificações e metodologias. As tabelas e gráficos fornecem dados percentuais sobre essas variáveis, com comparações entre regiões e portes de empresas.
This document summarizes the logistical preparations for the Eurojam scout camping event in Normandy, France. It discusses how the camp has been organized like a "city in the woods" with all necessary infrastructure and services, including 9.5 km of water piping and distribution of 400 radios. It highlights that most of the logistical work has been done by volunteer professionals sharing their skills with other scout volunteers. The camp setup allows 12,000 people to live comfortably in the forest for 10 days.
Desenvolvimento em projetos distribuídos e offshoreDiego Pacheco
O documento discute os desafios e estratégias para desenvolvimento de projetos de software distribuídos e offshore. Apresenta os principais desafios como comunicação entre equipes em locais diferentes, compartilhamento de conhecimento e diferenças culturais. Também sugere estratégias como uso de ferramentas como Skype e Redmine, pair programming, maratonas ágeis e demos frequentes para superar esses desafios.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
Mining of product reviews at aspect levelijfcstjournal
Today’s world is a world of Internet, almost all work can be done with the help of it, from simple mobile
phone recharge to biggest business deals can be done with the help of this technology. People spent their
most of the times on surfing on the Web; it becomes a new source of entertainment, education,
communication, shopping etc. Users not only use these websites but also give their feedback and
suggestions that will be useful for other users. In this way a large amount of reviews of users are collected
on the Web that needs to be explored, analyse and organized for better decision making. Opinion Mining or
Sentiment Analysis is a Natural Language Processing and Information Extraction task that identifies the
user’s views or opinions explained in the form of positive, negative or neutral comments and quotes
underlying the text. Aspect based opinion mining is one of the level of Opinion mining that determines the
aspect of the given reviews and classify the review for each feature. In this paper an aspect based opinion
mining system is proposed to classify the reviews as positive, negative and neutral for each feature.
Negation is also handled in the proposed system. Experimental results using reviews of products show the
effectiveness of the system.
Humans communication is generally under the control of emotions and full of opinions. Emotions and their opinions plays an important role in thinking process of mind, influences the human actions too. Sentiment analysis is one of the ways to explore user’s opinion made on any social media and networking site for various commercial applications in number of fields. This paper takes into account the basis requirements of opinion mining to explore the present techniques used to developed an full fledge system. Is highlights the opportunities or deployment and research of such systems. The available tools used for building such applications have even presented with their merits and limitations.
This document discusses techniques for classifying sentiments and mining opinions from text data. It begins with defining key terminology in opinion mining like opinion feature, sentiment, polarity, holder and time. It then discusses various data sources for opinion mining like blogs, reviews sites, datasets, microblogs and other text. It describes the granularity of opinion mining tasks at the document level, sentence level and feature level. Finally, it outlines approaches to opinion mining including supervised learning techniques like Naive Bayes, SVM and unsupervised learning techniques that use lexical resources without prior training. Evaluation metrics for sentiment classification systems like accuracy, precision, recall and F1 measure are also discussed.
Aspect-Level Sentiment Analysis On Hotel ReviewsKimberly Pulley
The document discusses aspect-level sentiment analysis on hotel reviews. It describes extracting sentiments on specific aspects or entities mentioned in documents, like reviews. It uses Python tools like scrapy and NLTK to preprocess reviews, identify aspects in sentences, and determine sentiment scores for each aspect using a sentiment analysis algorithm. The goal is to analyze different aspects of reviews and summarize sentiment values to understand customer feedback.
A proposed Novel Approach for Sentiment Analysis and Opinion Miningijujournal
as the people are being dependent on internet the requirement of user view analysis is increasing
exponentially. Customer posts their experience and opinion about the product policy and services. But,
because of the massive volume of reviews, customers can’t read all reviews. In order to solve this problem,
a lot of research is being carried out in Opinion Mining. In order to solve this problem, a lot of research is
being carried out in Opinion Mining. Through the Opinion Mining, we can know about contents of whole
product reviews, Blogs are websites that allow one or more individuals to write about things they want to
share with other The valuable data contained in posts from a large number of users across geographic,
demographic and cultural boundaries provide a rich data source not only for commercial exploitation but
also for psychological & sociopolitical research. This paper tries to demonstrate the plausibility of the idea
through our clustering and classifying opinion mining experiment on analysis of blog posts on recent
product policy and services reviews. We are proposing a Nobel approach for analyzing the Review for the
customer opinion
A proposed novel approach for sentiment analysis and opinion miningijujournal
as the people are being dependent on internet the requirement of user view analysis is increasing
exponentially. Customer posts their experience and opinion about the product policy and services. But,
because of the massive volume of reviews, customers can’t read all reviews. In order to solve this problem,
a lot of research is being carried out in Opinion Mining. In order to solve this problem, a lot of research is
being carried out in Opinion Mining. Through the Opinion Mining, we can know about contents of whole
product reviews, Blogs are websites that allow one or more individuals to write about things they want to
share with other The valuable data contained in posts from a large number of users across geographic,
demographic and cultural boundaries provide a rich data source not only for commercial exploitation but
also for psychological & sociopolitical research. This paper tries to demonstrate the plausibility of the idea
through our clustering and classifying opinion mining experiment on analysis of blog posts on recent
product policy and services reviews. We are proposing a Nobel approach for analyzing the Review for the
customer opinion.
This document summarizes a study that compares systematic and automated methods for sentiment analysis. The study extracted product features from online reviews of Samsung tablet PCs and used Naive Bayes classification to determine the positive, negative, and neutral sentiment distributions for each feature. Features like battery life had the highest positive sentiment, while cost had low positive sentiment. Weight had equal positive and negative sentiment. The study concludes the systematic approach provides more useful insight for product improvement than automated tools, which fail to identify specific sentiment-causing features.
Opinion mining of movie reviews at document levelijitjournal
The whole world is changed rapidly and using the current technologies Internet becomes an essential
need for everyone. Web is used in every field. Most of the people use web for a common purpose like
online shopping, chatting etc. During an online shopping large number of reviews/opinions are given by
the users that reflect whether the product is good or bad. These reviews need to be explored, analyse and
organized for better decision making. Opinion Mining is a natural language processing task that deals
with finding orientation of opinion in a piece of text with respect to a topic. In this paper a document
based opinion mining system is proposed that classify the documents as positive, negative and neutral.
Negation is also handled in the proposed system. Experimental results using reviews of movies show the
effectiveness of the system.
A Novel Voice Based Sentimental Analysis Technique to Mine the User Driven Re...IRJET Journal
This document presents a novel technique for sentiment analysis of user reviews using voice input. The proposed method uses speech recognition to convert spoken reviews to text, which is then analyzed using machine learning to classify the sentiment as positive, negative, or neutral. If implemented, this voice-based sentiment analysis could help organizations better understand customer opinions and help consumers make quicker decisions based on reviews. The system aims to scale well for different types of opinions and products.
Review on Opinion Mining for Fully Fledged Systemijeei-iaes
Humans communication is generally under the control of emotions and full of opinions. Emotions an d their opinions plays an important role in thinking process of mind, influences the human actions too. Sentiment analysis is one of the ways to explore user’s opinion made on any social media and networking site for various commercial applications in number of fields. This paper takes into account the basis requirements of opinion mining to explore the present techniques used to develop a fully fledged system. Is highlights the opportunities or deployment and research of such systems. The available tools used for building such applications have even presented with their merits and limitations.
A Subjective Feature Extraction For Sentiment Analysis In Malayalam LanguageJeff Nelson
The document discusses sentiment analysis of Malayalam film reviews using machine learning techniques. It proposes using Conditional Random Fields combined with rule-based approaches for sentiment analysis at the sentence and document level in Malayalam. The system is trained on a manually tagged corpus of over 30,000 tokens and tested on film reviews to determine the overall polarity (positive, negative, neutral) and rating of individual categories like film, direction, acting etc. The system achieved an accuracy of 82% in identifying sentiment and ratings.
Analyzing sentiment system to specify polarity by lexicon-basedjournalBEEI
Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
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.
The Role of Families and the Community Proposal Template (N.docxssusera34210
The Role of Families and the Community Proposal Template
(
Name of Presenter:
Focus of proposed presentation:
Age group your proposal will focus on:
)
Proposal Directions: Please complete each of the following sections of the proposal in order to demonstrate your competency in the area of the role that families and the community play in promoting optimal cognitive development. In each box, address the topic that is presented. The space for sharing your knowledge will expand with your text, so please do not feel limited by the space that is currently showing.
Explain how theory can influence the choices parents make when promoting their child’s cognitive development abilities for your chosen age group. Use specific examples from one theory of cognitive development that has been discussed this far in the course.
Explain how the environment that families create at home helps promote optimal cognitive development for your chosen age group. Provide at least two strategies that you would encourage parents to foster this type of environment.
Discuss the role that family plays in developing executive functions for your chosen age group. Provide at least two strategies that you suggest parents use to help foster the development of executive functions.
Examine the role that family plays in memory development for your chosen age group. Provide at least strategies parents can use to support memory development.
Examine the role that family plays in conceptual development for your chosen age group. Use ideas from your response to the Week 3 Discussion 1 forum to provide at least two strategies families can use to support development in this area.
Explain at least two community resources that would suggest families use to support the cognitive development of their children for your chosen age group.
Analyze of the role that you would play in helping to support families within your community to promote optimal cognitive development for your chosen age group.
Running Head: MINI-PROJECT: QUALITATIVE ANALYSIS 1
MINI-PROJECT: QUALITATIVE ANALYSIS 6
Mini-Project: Qualitative Analysis
Student’s Name
Institutional Affiliation
MINI-PROJECT: QUALITATIVE ANALYSIS
Introduction
It is important for qualitative data to be analyzed and the themes that emerge identified so that the data can be presented in a way that is understandable. Theme identification is an essential task in qualitative research and themes could mean abstract, often fuzzy, constructs which investigators identify before, during, and after data collection. I will discuss the themes that emerge from the data collected from the interview.Analyzing and presenting qualitative data in an understandable manner is a five step procedure that I will also explain in this paper.
Emergi ...
A NOVEL APPROACH FOR TWITTER SENTIMENT ANALYSIS USING HYBRID CLASSIFIERIRJET Journal
This document discusses a novel approach for Twitter sentiment analysis using a hybrid classifier. It begins with an abstract that outlines the goal of examining and analyzing Twitter sentiment during important events using a Bayesian network classifier and implementing principal component analysis for feature extraction. It then combines linear regression, XGBoost, and random forest classifiers. The results are evaluated based on accuracy, precision, recall, and F1-score metrics. The document then discusses challenges in sentiment analysis like co-reference resolution, association with time periods, sarcasm handling, domain dependency, negations, and spam detection that impact the sentiment analysis process.
This document reviews dictionary-based approaches to sentiment analysis. It discusses how sentiment analysis is used to determine sentiment polarity in text data using sentiment dictionaries like SentiWordNet. Dictionary-based methods involve matching words from a text to an opinion dictionary to determine if they express positive, negative, or neutral sentiment. The document also discusses some challenges with dictionary-based sentiment analysis, like handling negation and word sense disambiguation. Overall, the document provides an overview of dictionary-based sentiment analysis techniques and how they involve using sentiment dictionaries to classify the polarity of words and texts.
The document outlines the steps to request an assignment writing service from HelpWriting.net, including creating an account, providing assignment details in an order form, and reviewing writer bids before choosing a writer and placing a deposit to start the work. It notes the platform uses a bidding system and offers free revisions, and emphasizes original, high-quality work with refunds for plagiarism. The process aims to fully meet customer needs for assignment assistance.
Opinion Mining Techniques for Non-English Languages: An OverviewCSCJournals
The amount of user-generated data on web is increasing day by day giving rise to necessity of automatic tools to analyze huge data and extract useful information from it. Opinion Mining is an emerging area of research concerning with extracting and analyzing opinions expressed in texts. It is a language and domain dependent task having number of applications like recommender systems, review analysis, marketing systems, etc. Early research in the field of opinion mining has concentrated on English language. Many opinion mining tools and linguistic resources have been built for English language. Availability of information in regional languages has motivated researchers to develop tools and resources for non-English languages. In this paper we present a survey on the opinion mining research for non-English languages.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 6
H046025258
1. Kapil Verma Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 2), June 2014, pp.52-58
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Identification and Opinion Extraction throughUser Generated
Content on Web Based Social Media
Dr. Deepak Arora, Kapil Verma
Professor, Dept. of computer science and Engineering (ASET)Amity University Uttar PradeshLucknow,India
Sr. Lecturer,Dept. of computer science and EngineeringBabuBanarasi Das Northern India Institute of
Technology(BBDNIIT),Uttar Pradesh,Lucknow,India
Abstract
Nowadays internet is becoming a platform where different user can post there ideas and opinions. The social
networking sites and blogs offer a wide variety of such informative text which can be used to establish or
determine a mindset for a particular product, person or individual. These blogs can be used as a vast source of
information through which one can predict opinion as well as planning for different business strategies. Due to
huge amount of information there is always need of specific tool or approach to mine useful text called opinion.
Authors have proposed an approach of mining and classification for different real time datasets gathered from
various sources of information, freely available on internet. Authors have tested the approach over these datasets
and found suitable results. In this paper we propose a method that classifies a user-generated content on the
basis of positive, negative, neutral, double negative, negative positive, triple negative.Authors has proposed
rules for analyzing ideas and tested against dataset using Naive Bayes and Support Vector machine (SVM)
model for accuracy and found best result 80.39 % for NB and 81.37 % for SVM.
IndexTerms— Double Negative, Triple Negative, Negative Positive, 64 rules for polarity.
I. INTRODUCTION
Public sentiment is everything. With public
sentiment, nothing can fail. Without it, nothing
can succeed.
Abraham Lincoln
Opinion mining has now become very popular
research field. Many research papers have already
been published on various national and international
journals and many researchers have made profuse and
significant progress in opinion mining and still this
research is in progress, there is always need of specific
tool or approach to mine the useful text called opinion.
Author proposed an approach which is useful for
determining the orientation of user generated content
on web based social media. Web based social media
offer a wide variety of informative text, for example
reviews about any product, views about an individual,
liking and disliking of services etc. These information
can be use for a variety of purposes in different area.
Product reviews are helpful for customers. When
individual go to shop any product they want reviews
of other customers about the product. These reviews
are helpful for determining mindset for particular
product. Reviews would be more helpful for business
purpose when machine correctly understand the
natural language and sentiments of human in text.
There are various sites (platform) where people can
express their opinion and share ideas about any topic
.There exist some opinion mining software and tools
in the market that gives opinion for the keyword
searched but that are not much effective. Often it
analyze for positive, negative and neutral words in the
sentence. In this paper authors have taken up those
sentences that are constructed using positive, negative,
neutral, double negative, negative positive and triple
negative. Extract public opinion from the user
generated content [9]. The basic work included in
analysis of sentences is to classify in to positive,
negative or neutral [2]. A different approach is used in
determining the sentiment of sentence. Instead of
classifying sentence only for positive, negative or
neutral, we are using six parameters for classifying the
sentiments of sentences: negative, positive, neutral,
double negative, negative positive and triple negative.
Some time the meaning of word is changed
completely when using the two or three polarity words
together in the same sentence. On the basis of these
six parameters we can easily determine the sentiment
of sentence. For example,
1- Afzal Guru didn‘t kill anybody.
2- Child actor/actress doesn‘t dislike not going to
school.
3- I hate it whenever fake people say they hate fake
people. It is like a double negative...... Isn‘t
it??
4- ―Didn‘t Have Nothing To Do With Drugs‖
5- Breast Cancer, "I Won't Back Down!"
6- When I was a commissioner there I never did
nothing nowhere near that.
RESEARCH ARTICLE OPEN ACCESS
2. Kapil Verma Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 2), June 2014, pp.52-58
www.ijera.com 53|P a g e
7- The blanket didn't barely protect their shivering
bodies.
The above seven sentences are double negative
and triple negative sentences. If you look at first and
second sentence ―Afzal Guru didn‘t kill anybody‖,
―Child actor/actress don‘t dislike not going to school‖.
The second sentence actually means that the child
actor/actress dislike going to school but there are three
negative words which change the focus that whether
the child actor/actress actually likes to go to school or
not.In the first sentence two negative words used
together so it basically become positive, just like
calculation in mathematics. ―Afzal guru didn‘t kill
nobody‖ would mean that the man did kill someone
.In the above other sentences three, four, five, six, and
seven. These sentences also contain double or triple
negative words.
In negative positive words a negative word comes
just before positive word and changes its meaning
from positive to negative. For example,
―The quality of ‗X‘ mobile phone Camera is not
good.‖
This review sentence contain one positive word
and its meaning changes from positive to negative
because a negative word ‗not‘ is written before it. List
of positive words and negative words is given[6] . In
this paper we particularly focus on six parameter and
rules on the basis of that we determine the polarity of
sentences. In section 2 related work and source of
motivation for the ongoing research is discussed.
Section 3 explains the process of mining reviews.
Section 4 presents Method and rules that determine
the polarity of sentence. In section 5 we discuss the
findings of this research and result are discuss in
section 6.
II. RELATED WORK
Social media provide abundance of information in
the form of customer review. It is one of the current
hot research topic for many researchers .Foundation
and trends in information retrieval [3] discussed it in
details. In there research, the author describe opinion
oriented classification and opinion oriented
Information retrieval. Classifying the opinion
document as positive and negative [5].Method to
collect a corpus with positive and negative sentiments
is given in [1].In this author explain the method which
allow to collect negative and positive sentiments such
that no human effort is needed for classifying the
document. Supervised learning model to determine if
sentiment expressed on different topics in a
conditional sentence [8] are positive, negative or
neutral. Conditional sentence are the sentences that
describe the hypothetical situation and there
consequences .In my work sentence is analyze on the
basis of six parameters and sixty four rule and
exceptions governing to decide polarity of sentences.
Other similar work comes fromfeature base
sentiment analysis [4] in this work the product feature
such as design,quality etc of any product and opinion
have been expressed on them. Some times opinion is
positive on one feature and negative on other.
The subjective orientation have been studied [10]
uses the adjective to find the sentence subjectivity.
Hatzivassiloglou and wiebe [11] works on sentence
subjectivity. They propose a method that search for
adjective then reflect the negative or positive
orientation. Comparative sentence is also discussed in
the study [7]. Most of the user on internet wants to
post an idea and opinion on the internet. Daily new
forums are created to discuss on various topics. If any
new product launched, people start discussing about it
by sharing ideas and opinion in their own way on
internet forum. Some express anger, flaws, some
speak in favors of etc. Most of the time a biased
decision has been taken due to limited accessibility of
opinion in the form of reviews. So there is always a
need of specific tool or approach to mine the useful
text called opinion. For this reason it is necessary to
find some approach that understand natural language
and sentiment expressed by human in text .Many
researcher is collectively doing research in this field
for making automated system that understand exactly
the same as human being .Many researchers actively
working on it in many countries all over the world.
III. MODEL FOR OPINION MINING
PROCESS
The following figure depicting the overall process
of opinion mining-
Fig. 1. Model for Opinion Mining Process
In the above figure user put keyword in the user
interface in response user interface process the query
and start crawling the opinion based sentences from
the web. It search opinionated sentences from forum,
blogs, social networking sites and extracted
3. Kapil Verma Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 2), June 2014, pp.52-58
www.ijera.com 54|P a g e
thatopinionated sentences. Then it performs the
parsing of sentences in to the words. Then perform
classification of part of sentences (POS). Comparing it
with the list of words for six parameters then we rank
the word for each parameter in zero and one for the
parameter present or absent in the sentence. A related
work is done in opinion retrieval from blog [12].Then
by ranking method, sixty four rules and exceptions we
find the orientation of the sentences and give result
through the user interface.
IV. METHOD AND RULES FOR
DETERMINING POLARITY OF
SENTENCES.
In this paper authors are using six parameters for
identifying the sentiment of sentences. Thease six
parameter are:
Negative, Positive, Neutral, Double Negative,
Negative Positive, & Triple negative and their codes
are ‗a‘, ‗b‘, ‗c‘, ‗d‘, ‗e‘, &‘f‘ respectively.
A. Negative Parameter
A parameter that expresses, containing, or
consisting of negation. It refuses and denying the
request and gives negative answer. e.g. no, not, never,
scary etc. In this paper author uses code ‗a‘ for
negative parameter.
B. Positive Parameter
A parameter that shows acceptance, affirmation
and positive response of a product, appraisal of
service .e.g. good ,graceful ,happy etc. In this paper
author uses code ‗b‘ for positive parameter.
C. Neutral Parameter
A parameter that expresses no orientation.e.g.
person, nation, country etc. In this paper author uses
code ‗c‘ for neutral parameter.
D. Double Negative Parameter
Double negative is used as a positive parameter.
It gives the positive orientation. Basically the
construction of double negative parameter is –
a + a = b. (1)
Note: The above equation it is just like math when
two negative parameter added it will give positive
parameter.
E. Negative Positive Parameter
A negative positive parameter is used as a
negative parameter. It gives the negative sense .The
basic construction of negative positive parameter is –
a * b = a. (2)
F. Triple Negative Parameter
A triple negative parameter is used as a negative
parameter. When negative parameter is used three
times. First two negative will become positive by ―Eq
. 1‖ and when third negative is used with this positive
it will become negative by ―Eq. 2‖ .The basic
construction of triple negative parameter is –
a * a * a = b (3)
A. Determining The Polarity Of Sentences
The polarity of sentence is determined by the six
parameter in the ―TABLE I.‖.Analysis of sentences is
done by marking it 0 and 1.Mark 1 to those parameter
if the sentence have word for that parameter. Mark 0
to those parameter if the sentence have no word for
that parameter. Result is declared in the last column.
B. Rules For Determining The Polarity
There are total six parameter for the value 0 and
1.So the combination six parameter will be sixty four.
2 raise to the power 6 i.e. 2 x2 x 2 x 2 x 2 x 2
=64.
Result is declared in the last column as negative
(-ve), positive (+ve) and Neutral. Exceptions (Excp.)
are also there in the result column.
So there will be total sixty four rule for determining
the polarity are given in the TABLE I below.
TABLE I. 64 Rules For Polarity
Sl No Six Parameter
a b c d e f Result
Rule1 0 0 0 0 0 0 Neutral
Rule2 0 0 0 0 0 1 -ve
Rule3 0 0 0 0 1 0 -ve
Rule4 0 0 0 0 1 1 -ve
Rule5 0 0 0 1 0 0 +ve
Rule6 0 0 0 1 0 1 Excp.
Rule7 0 0 0 1 1 0 -ve
Rule8 0 0 0 1 1 1 Excp.
Rule9 0 0 1 0 0 0 Neutral
Rule10 0 0 1 0 0 1 -ve
Rule11 0 0 1 0 1 0 -ve
Rule12 0 0 1 0 1 1 +ve
Rule13 0 0 1 1 0 0 +ve
Rule14 0 0 1 1 0 1 Excp.
Rule15 0 0 1 1 1 0 Excp.
Rule16 0 0 1 1 1 1 Excp.
Rule17 0 1 0 0 0 0 +ve
Rule18 0 1 0 0 0 1 -ve
Rule19 0 1 0 0 1 0 -ve
Rule20 0 1 0 0 1 1 +ve
Rule 21 0 1 0 1 0 0 +ve
Rule22 0 1 0 1 0 1 Excp.
Rule23 0 1 0 1 1 0 Excp.
Rule24 0 1 0 1 1 1 Excp.
Rule25 0 1 1 0 0 0 +ve
Rule26 0 1 1 0 0 1 Excp.
Rule27 0 1 1 0 1 0 Excp.
4. Kapil Verma Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 6( Version 2), June 2014, pp.52-58
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Rule28 0 1 1 0 1 1 Excp.
Rule29 0 1 1 1 0 0 +ve
Rule30 0 1 1 1 0 1 Excp.
Rule31 0 1 1 1 1 0 Excp.
Rule32 0 1 1 1 1 1 Excp.
Rule33 1 0 0 0 0 0 -ve
Rule34 1 0 0 0 0 1 +ve
Rule35 1 0 0 0 1 0 +ve
Rule 36 1 0 0 0 1 1 -ve
Rule37 1 0 0 1 0 0 -ve
Rule38 1 0 0 1 0 1 Excp.
Rule39 1 0 0 1 1 0 Excp.
Rule40 1 0 0 1 1 1 Excp.
Rule41 1 0 1 0 0 0 -ve
Rule42 1 0 1 0 0 1 Excp.
Rule43 1 0 1 0 1 0 Excp.
Rule44 1 0 1 0 1 1 -ve
Rule45 1 0 1 1 0 0 Excp.
Rule46 1 0 1 1 0 1 Excp.
Rule47 1 0 1 1 1 0 Excp.
Rule48 1 0 1 1 1 1 -ve
Rule49 1 1 0 0 0 0 -ve
Rule50 1 1 0 0 0 1 Excp.
Rule51 1 1 0 0 1 0 Excp.
Rule52 1 1 0 0 1 1 -ve
Rule53 1 1 0 1 0 0 Excp.
Rule54 1 1 0 1 0 1 Excp.
Rule55 1 1 0 1 1 0 Excp.
Rule56 1 1 0 1 1 1 -ve
Rule57 1 1 1 0 0 0 -ve
Rule58 1 1 1 0 0 1 Excp.
Rule59 1 1 1 0 1 0 Excp.
Rule60 1 1 1 0 1 1 -ve
Rule61 1 1 1 1 0 0 Excp.
Rule62 1 1 1 1 0 1 Excp.
Rule63 1 1 1 1 1 0 Excp.
Rule64 1 1 1 1 1 1 -ve
The above sixty four rules are sufficient for
determining the polarity of sentence. There are
exceptions in the sixty four rules because some of
rules are not sufficient for directly determining the
polarity of sentence. So thirty two exceptions are
included in sixty four rules.
Exception rule are made based on seven
coordinating conjunction (CC) and some
subordinating conjunction (SC) which are given
below.
Coordinating conjunction are AND, Or, But, Nor,
So, For and Yet. Subordinating conjunction are Even
Though, Even If, Once, Provided, and While.
Conjunctions are used to join part of sentences
(POS).There are seven coordinating conjunction used
to join part of sentences (POS). Below, discussing
seven CC and some SC.
AND: It is very common conjunction used to join
POS. It is observed that one POS is the result of
another POS. So the polarity word appears after AND
will be the result.
OR: It is also very common conjunction use to show
possibility, correction, and polarity condition. For
example, ―UPA must decrease inflation or they would
not be in 2014‖.
BUT: It is used to connect to two clauses with contrast
in there meaning. Scan the part of sentence after but
conjunction .The polarity word used after BUT will be
the result.
NOR: It s generally used with neither nor. Mostly it is
used with negative.It takes the sentiment of sentences
towards negative.
SO: It is used to show the reflection of two individual
clauses. It gives the over all sentiment of sentence. So
the sentiment word used after SO will decide the
result.
FOR: It is a conjunction use to give reason for the
POS use before for. So result will be decided by
sentiment word in POS before FOR.
YET: It has several uses. We can use it as still, adding,
even etc. For Example, ―public complaining loudly
about increasing rape cases, yet government not
taking any action‖.
Even though: It is subordinating conjunction. Its
projection is based on first part. For Example
―X is my good friend, even though X is smoker‖.
Even IF: It is often use to give condition in sentences.
For Example, ―Credibility of Nokia will not decrease
even if one of its products fails‖.
Once: It has ambiguity. Some times it use in respect of
time and some time it is use as situation. For example
, ― I‘ll go and watch movie, once I finish my
dissertation work‖. First half of the sentences is
depend on the second half.
Provided: It gives the condition. First half is depend
on the second half. For Example, ―BSNL is good
company, provided it improves services‖.
While: It is also a condition. It is observed orientation
of sentences is based on sentiment word after while.
For example, ―I‘ll stay here while raining‖.
V. RESULT AND DISCUSSIONS
A.DATA SETS
In our research, author have presented the six
parameters for analysis of sentences, model for
opinion mining process and method and rule for
determining polarity of sentences. We use dataset
from the different sources freely available on internet.
Our main consideration in collecting dataset is to
include actual data from different social networking
sites, blogs, and other review related sites. We have
also taken sentences that contain six parameters and
also contain conjunction in the sentences. We then
construe many sentences from different sources. We
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ISSN : 2248-9622, Vol. 4, Issue 6( Version 2), June 2014, pp.52-58
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also construe the conjunction in the sentences .We
have noted pattern use in sentences. In our
observation, we noted pattern in which people use tag
line of product instead name of product to comment.
Many of the sentences have no orientation i.e.
positive, negative. We put that sentence in the neutral
category. But our main consideration in this research
is to analyze the sentences on the basis of six
parameter and conjunction. We have construed our
analysis of sentence nearly around 102 selected
sentences. In section IV we declare the six parameters.
Table I contain the sixty four rule. It includes all the
possible combination of six parameters. We used the
kappa score for computing inter rated agreement. We
achieved two score 0.6663 for naïve bayes classifier
and 0.6891 for support vector machine(SVM).This
shows SVM has strong agreement than naïve bayes .
B.EXPERIMENTAL RESULT
Now we show the experimental result of user
generated content. We compare the result of naïve
bayes and SVM implementation, which shows the
effective result. We use 10 fold cross validation for
the best result.
Three Class classifications:
We have taken result for two different classifiers.
One is naïve bayes and other is Support vector
machine (SVM).We use negative, positive, and
neutral as three class classification. We have shown
the result in Table III for two classifiers. We have
computed TP Rate, FP Rate, Precision, Recall, F-
Measure and ROC Area for three classes.
For this we have taken 102 sentences to perform
experiment. Although accuracy for the model naïve
bayes is 80.39 % and SVM 81.37 %.which yielded
accuracy for naïve bayes is lower. Table II shows the
confusion matrix for Naïve bayes and SVM
respectively. This shows that, from out of 102 sample
sentences 42 are negative, 48 are positive and 12 are
negative.
TABLE II. Confusion Matrix
Confusion Matrix NB
Classes
Negat
ive
Positi
ve
Neutra
l
Negative 29 12 1
Positive 5 43 0
Neutral 1 1 10
Confusion Matrix SVM
Classes
Negat
ive
Positi
ve
Neutra
l
Negative 37 4 1
Positive 11 36 1
Neutral 1 1 10
In Table II confusion matrix, for NB of 42 actual
negative, this model predicted 29 were negative, 12
were positive , and 1 were neutral. For SVM of 42
actual negative, this model predicted 37 were
negative, 4 were positive, and 1 were neutral.
Similarly we can see for other class positive and
neutral. These conclude that confusion matrix for
SVM is better because accuracy for SVM is 81.37 %.
TABLE III. Accuracy by class using NB & SVM
Accuracy by class using Naïve Bayes
T
P
Ra
te
FP
Rate
Prec
.
Rec
.
F
ROC
Area
Class
1 0.
69
0.1
0.82
9
0.6
9
0.7
53
0.90
8
Negat
ive
2 0.
89
6
0.24
1
0.76
8
0.8
96
0.8
27
0.90
7
Positi
ve
3 0.
83
3
0.01
1
0.90
9
0.8
33
0.8
7
0.96
1
Neutr
al
4 0.
80
4
0.15
6
0.80
9
0.8
04
0.8
02
0.91
4
Wt.Av
g.
Accuracy by class using SVM
T
P
Ra
te
FP
Rate
Prec
.
Rec
.
F
ROC
Area
Class
1 0.
88
1
0.2
0.75
5
0.8
81
0.8
13
0.87
6
Negat
ive
2 0.
75
0.09
3
0.87
8
0.7
5
0.8
09
0.86
6
Positi
ve
3 0.
83
3
0.02
2
0.83
3
0.8
33
0.8
33
0.94
3
Neutr
al
4 0.
81
4
0.12
9
0.82
2
0.8
14
0.8
14
0.87
9
Wt.Av
g.
Fig. 2. Shows the threshold curve for negative
,positive, and neutral .In Fig 2 a,b,c represents
negative, positive , and neutral respectively for NB
and d,e,f represents negative, positive , and neutral
respectively for SVM .The X-Axis represents the FP-
Rate and Y-Axis represent TP-Rate .Table III shows
the accuracy for different accuracy measure‘s
parameter .When we compare it with each other we
found weighted average TP Rate for NB(Naïve Bayes)
were 0.804 and for SVM were 0.814.Similarly FP
Rate for NB were 0.156 and for SVM were
0.129.Precision and Recall for NB were 0.809 and
0.804 respectively and for SVM were 0.822 and 0.814
respectively. F measure for NB were 0.802 and for
SVM were 0.814.It is clearly seen that SVM yielded
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www.ijera.com 57|P a g e
good accuracy. So we use SVM that gives best result
using 10 foldcross validation.
So we finally found SVM yielded better result.
Fig. 2.Threshold curve For Negative Positive and
Neutral. a,b,c represents negative ,positive and
Neutral respectively for NB. andd,e,f represents
negative ,positive, and neutral respectively for
SVM.
VI. CONCLUSION AND FUTURE WORK
To perform Identification and Opinion Extraction
through User generated Content on Web Based Social
Media accurately, author propose and implement
approach with the help of datasets. This paper studied
sixty four rules for polarity and conjunction used in
sentences. It is unlikely that there is common solution
for all problems. Authors have taken seven
coordinating conjunctions and some subordinating
conjunctions. Our work was carried out by
computational and linguistic pattern. In linguistic
study, authors focused on sentence pattern after using
conjunction, which have been explained through
useful result in section v. In computational study,
authors have done classification through NB model
and SVM model to predict accuracy of whether
opinions on topics are positive, negative or neutral.
Experimental found the best result for the datasets.
In Future work, author will improve the
classification accuracy and study more in linguistic for
identifying orientation of topic. Although there are
many different conjunctions uses in the sentences,
author will use all these conjunction in the sentences
for determining the polarity of sentences.
VII.ACKNOWLEDGMENT
The authors are very thankful to their respected
Mr. AseemChauhan, Chairman, Amity University,
Lucknow, Maj. Gen. K.K. Ohri, AVSM (Retd.), Pro
Vice Chancellor, Amity University, Lucknow, India,
for providing excellent computation facilities in the
University campus. Authors also pay their regards to
Prof. S.T.H. Abidi, Director and Brig. U.K. Chopra,
Deputy Director, Amity School of Engineering, Amity
University, Lucknow for giving their moral support
and help to carry out this research work.
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