This document provides a review of sentiment mining and related classifiers. It begins with an introduction to data mining and web mining. It then discusses related work on applying techniques like content, descriptive and network analytics to tweets to gain supply chain insights. The document also covers the basic workflow of opinion mining including preprocessing, feature extraction and selection, and feature weighting. It compares classifiers like Naive Bayes, decision trees, k-nearest neighbor, and support vector machines. Finally, it discusses applications of sentiment analysis in areas like commercial markets, products, maps, software, and voting. It also discusses the importance of opinion mining in governance.
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.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
APPLYING OPINION MINING TO ORGANIZE WEB OPINIONSIJCSEA Journal
Rapid increase of opinions on the web requires an effectual system to organize opinions. Opinion mining is a realistically plot and demanding field devoted to detect subjective content in text documents. If opinions are non-structured then it’s difficult for customers and organizations to understand. This study proposes an approach focusing on designing a system to organize web opinions at the time when user is posting, before actually being extracted by expertise. New system (Opinion Organization System) provides four stages. In first stage, it provides a list of several product categories and user selects at least one. In second stage, a list of selected product relevant features is displayed to the user. In third stage, user firstly selects features for which wants to express opinions, then uses polarity based P set and N set containing adjective words list and in fourth stage, uses thumb selection table to add opinions.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Design of Automated Sentiment or Opinion Discovery System to Enhance Its Perf...idescitation
In today’s social networking era, if one has to make
decision about any product, service or individual performance,
the availability of various comments, suggestions, ratings,
and feedbacks are abundant. The required decision support
data can be collected through different sources of Medias like
newspapers, blogs, and discussion forums and from internet
too. So surely, it leads to the selection of best product, service
or individual if it is analyzed efficiently. In leading and
competitive world, this is huge and practical need of industries,
organizations to empower their qualities. In the recent years,
the significant study is done in the field of sentiment analysis.
However, the earlier work focused the implementation and
evaluation of individual sub technique of sentiment analysis.
Though these implementations produces significant results
of sentiment or opinion analysis, the trust of decision makers
is still in dangling to accept the results of such analysis. In
this paper, initially, we have been described the brief review
about the sentiment or opinion analysis system. Then the
details are provided about the design and about how to build
an automated opinion discovery system to enhance
performance of sentiment or opinion analysis based on feature
extraction sentiment analysis sub technique, natural language
processing and data mining techniques in an integrated way
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
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.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
APPLYING OPINION MINING TO ORGANIZE WEB OPINIONSIJCSEA Journal
Rapid increase of opinions on the web requires an effectual system to organize opinions. Opinion mining is a realistically plot and demanding field devoted to detect subjective content in text documents. If opinions are non-structured then it’s difficult for customers and organizations to understand. This study proposes an approach focusing on designing a system to organize web opinions at the time when user is posting, before actually being extracted by expertise. New system (Opinion Organization System) provides four stages. In first stage, it provides a list of several product categories and user selects at least one. In second stage, a list of selected product relevant features is displayed to the user. In third stage, user firstly selects features for which wants to express opinions, then uses polarity based P set and N set containing adjective words list and in fourth stage, uses thumb selection table to add opinions.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Design of Automated Sentiment or Opinion Discovery System to Enhance Its Perf...idescitation
In today’s social networking era, if one has to make
decision about any product, service or individual performance,
the availability of various comments, suggestions, ratings,
and feedbacks are abundant. The required decision support
data can be collected through different sources of Medias like
newspapers, blogs, and discussion forums and from internet
too. So surely, it leads to the selection of best product, service
or individual if it is analyzed efficiently. In leading and
competitive world, this is huge and practical need of industries,
organizations to empower their qualities. In the recent years,
the significant study is done in the field of sentiment analysis.
However, the earlier work focused the implementation and
evaluation of individual sub technique of sentiment analysis.
Though these implementations produces significant results
of sentiment or opinion analysis, the trust of decision makers
is still in dangling to accept the results of such analysis. In
this paper, initially, we have been described the brief review
about the sentiment or opinion analysis system. Then the
details are provided about the design and about how to build
an automated opinion discovery system to enhance
performance of sentiment or opinion analysis based on feature
extraction sentiment analysis sub technique, natural language
processing and data mining techniques in an integrated way
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Analyzing target user group¡¦s preferences and product form design specificat...Waqas Tariq
In the modern market where consumerism is running higher and the product life span is getting shorter, it is one of the challenges for the marketing and design departments in enterprises to know how to get a thorough grasp of the consumer¡¦s preference and potential target user group. With the wide spread and growth of the internet, a web-based survey is not influenced by time and space factors, making it easier for designers to have an in-depth understanding of the consumer¡¦s preferences towards products. Based upon the 2-dimensional image scale, 120 college students from Taiwan and Japan were invited to evaluate 27 pencil sharpener samples in terms of their preferences and intention of purchase. From the survey, competitive portable pencil sharpeners were identified for the references of new product design and development. The results indicated that such a web-based 2-dimensional image survey system could offer real time help in product segmentation and the selection of competition products as well as the target user group with the output systematic diagrams and tables. Furthermore, morphological analysis for product form elements and quantification type I analysis could help designers and marketing managers set up proper policies for product form design for the target user groups in the design and marketing of new product development.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant
contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity
logs and product items, accurate and efficient recommendation is a challenging computational task. This
paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)
algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for
collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be
analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of
applications according to the accessibility of data sources by carefully adjust the weights of different data
sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product
co-clusters. The results show that our proposed approach provide more meaningful recommendation
results, and outperforms existing item-based and user-based collaborative filtering recommendations in
terms of accuracy and ranked position.
Framework for opinion as a service on review data of customer using semantics...IJECEIAES
At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
Assessment of Internet Banking Services Continued Use: Role of Socio-cognitiv...TELKOMNIKA JOURNAL
Recently, continued use of information systems in general and particularly the context of internet
banking has drawn increasing attention both in academic and trade literatures. However, different
perspectives that determine the continued use of internet banking has been addressed. Given the long-term
goal of any business entity is to increase its productivity, expand its customer base and maximize revenues,
it is crucial for banks offering internet banking services to focus on encouraging their customers to continually
use internet banking. While emerging attention had been given on the assessment of continued use of
internet banking services from single view, little of attention were given from researchers to combine different
views in a single comprehensive model. Yet, it is believe that combining different views would provide a
better understanding to the issues surrounding the continued use. Drawing on the literature, this study
developed a model to determine factors that influence the continued use of internet banking by combining
factors from the socio-cognitive view and the relational view. Using a survey questionnaire, 450
questionnaires were distributed to users of internet banking in Libya. A number of interesting findings
emerged, among others, is the lack of association between trust and perception to continue using the internet
banking services in Libya.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
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
Recent Developments and Analysis of Electromagnetic Metamaterial with all of ...IOSR Journals
Recent advances in metamaterials (MMs) research have highlighted the possibility to create novel
devices with electromagnetic functionality. The metamaterial have the power which can easily construct
materials with a user-designed EM response with a particular target frequency. This is the important
phenomena of THz frequency region that can make a considerable progress in design fabrication, and define the
characteristics of MMs at THz frequencies. This article illustrates the latest advancements of THz MMs
research.
Multi-Element Determination of Cu, Mn, and Se using Electrothermal Atomic Abs...IOSR Journals
Simultaneous multi-element graphite furnace atomic absorption spectrometer (SIMAA 6000) is used to get a new multi-element determinations methodology for Cu, Mn, and Se. Firstly, the optimum conditions for single-element mode are determined (which include: pyrolysis and atomization temperatures). Secondly, the optimum conditions for multi-element mode are also determined. The conditions in the two modes have been compared in terms of the characteristic masses, detection limits and pyrolysis and atomization temperatures. The effect of the matrix on the determination has been studied using urine standard sample from Seronorm (LOT 0511545). The accuracy of the developing methods has been confirmed by analysis different biological reference materials. Simultaneous multi-element GF-AAS offers a rapid, low cost and sensitive method for the analysis of trace elements
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Analyzing target user group¡¦s preferences and product form design specificat...Waqas Tariq
In the modern market where consumerism is running higher and the product life span is getting shorter, it is one of the challenges for the marketing and design departments in enterprises to know how to get a thorough grasp of the consumer¡¦s preference and potential target user group. With the wide spread and growth of the internet, a web-based survey is not influenced by time and space factors, making it easier for designers to have an in-depth understanding of the consumer¡¦s preferences towards products. Based upon the 2-dimensional image scale, 120 college students from Taiwan and Japan were invited to evaluate 27 pencil sharpener samples in terms of their preferences and intention of purchase. From the survey, competitive portable pencil sharpeners were identified for the references of new product design and development. The results indicated that such a web-based 2-dimensional image survey system could offer real time help in product segmentation and the selection of competition products as well as the target user group with the output systematic diagrams and tables. Furthermore, morphological analysis for product form elements and quantification type I analysis could help designers and marketing managers set up proper policies for product form design for the target user groups in the design and marketing of new product development.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant
contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity
logs and product items, accurate and efficient recommendation is a challenging computational task. This
paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)
algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for
collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be
analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of
applications according to the accessibility of data sources by carefully adjust the weights of different data
sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product
co-clusters. The results show that our proposed approach provide more meaningful recommendation
results, and outperforms existing item-based and user-based collaborative filtering recommendations in
terms of accuracy and ranked position.
Framework for opinion as a service on review data of customer using semantics...IJECEIAES
At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
Assessment of Internet Banking Services Continued Use: Role of Socio-cognitiv...TELKOMNIKA JOURNAL
Recently, continued use of information systems in general and particularly the context of internet
banking has drawn increasing attention both in academic and trade literatures. However, different
perspectives that determine the continued use of internet banking has been addressed. Given the long-term
goal of any business entity is to increase its productivity, expand its customer base and maximize revenues,
it is crucial for banks offering internet banking services to focus on encouraging their customers to continually
use internet banking. While emerging attention had been given on the assessment of continued use of
internet banking services from single view, little of attention were given from researchers to combine different
views in a single comprehensive model. Yet, it is believe that combining different views would provide a
better understanding to the issues surrounding the continued use. Drawing on the literature, this study
developed a model to determine factors that influence the continued use of internet banking by combining
factors from the socio-cognitive view and the relational view. Using a survey questionnaire, 450
questionnaires were distributed to users of internet banking in Libya. A number of interesting findings
emerged, among others, is the lack of association between trust and perception to continue using the internet
banking services in Libya.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
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
Recent Developments and Analysis of Electromagnetic Metamaterial with all of ...IOSR Journals
Recent advances in metamaterials (MMs) research have highlighted the possibility to create novel
devices with electromagnetic functionality. The metamaterial have the power which can easily construct
materials with a user-designed EM response with a particular target frequency. This is the important
phenomena of THz frequency region that can make a considerable progress in design fabrication, and define the
characteristics of MMs at THz frequencies. This article illustrates the latest advancements of THz MMs
research.
Multi-Element Determination of Cu, Mn, and Se using Electrothermal Atomic Abs...IOSR Journals
Simultaneous multi-element graphite furnace atomic absorption spectrometer (SIMAA 6000) is used to get a new multi-element determinations methodology for Cu, Mn, and Se. Firstly, the optimum conditions for single-element mode are determined (which include: pyrolysis and atomization temperatures). Secondly, the optimum conditions for multi-element mode are also determined. The conditions in the two modes have been compared in terms of the characteristic masses, detection limits and pyrolysis and atomization temperatures. The effect of the matrix on the determination has been studied using urine standard sample from Seronorm (LOT 0511545). The accuracy of the developing methods has been confirmed by analysis different biological reference materials. Simultaneous multi-element GF-AAS offers a rapid, low cost and sensitive method for the analysis of trace elements
Dietary Supplementation with Calcium in Healthy Rats Administered with Artemi...IOSR Journals
Reports on the role of calcium on predisposition to cardiovascular disease have been rather inconsistent while studies on its interaction with other medications are ongoing. We therefore investigated the effect of separate and combine administration of calcium supplement with artemisinin-based combination drug on hepatic and serum lipid profile. Thirty two male wistar rats were randomly assigned into four groups of eight rats each. The control (group A) received normal saline. Group B and D were placed on 10mg/Kg calcium twice daily for four weeks. On the thirtieth day, therapeutic dose of artemisinin-based combination was simultaneously administered to group C and group D twice daily for three days. All the rats were then sacrificed after 12 hours fasting, blood was withdrawn and the liver removed and homogenized in an appropriate buffer. Biochemical analysis showed no significant (p>0.05) variation in hepatic triaacylglycerol in all the treated groups whereas calcium supplementation was observed to induce a significant (p<0.05) reduction in hepatic cholesterol. Significant elevations due to calcium supplementation were also observed in serum total cholesterol, LDL cholesterol level and atherogenic risk index with a concomitant reduction in serum HDL cholesterol. No significant change was observed in serum total cholesterol, triacylglycerol and serum lipoproteins in all other treatment groups. Our study suggests that calcium supplementation may predispose to cardiovascular disease and that its co administration with ACT may not aggravate nor reduced the predisposition risk.
Properties of CdS Chemically Deposited thin films on the Effect of Ammonia Co...IOSR Journals
The effect of ammonia concentration on electrical properties, optical properties and structural properties of chemical bath deposited (CBD) Cadmium sulphide (CdS) thin films has been revealed. The films were prepared by using cadmium acetate as cadmium ion (Cd2+) source, thiourea as sulphur ion (S2-) source and ammonia as the complexing agent. Ammonia concentration was changed from 0.1 M – 3.0 M. Ammonia concentration at 2.0 M uniform, dense and continuously coated films were obtained. Not only typical cadmium-pure but also unusual sulphur deficiency phenomena were observed for CBD CdS thin films. In the present investigation, the carrier concentration varied form 1.831X106cm-3 to 1.026X106cm-3 when ammonia concentration is changed from 0.5M to 2.5 M. The direct band gap energy at 0.5M is 1.92eV while at 2.5M is 2.65eV. The surface morphology of as deposited thin films is almost smooth and no grains were observed clearly at low molar concentration and predominant grains at the concentration of ammonia is 2.0M. By estimated Cd:S ratio value is found to be 1.04 by using EDAX. The thin film deposited at 2.0M concentration shows the highest degree crystallinity. The formation mechanism of the films with various ammonia concentrations is discussed.
Prediction of Consumer Purchase Decision using Demographic Variables: A Study...IOSR Journals
The demographic environment is of major interest to marketers because it involves people and people make up market. Fragmentation of the mass market into numerous micro markets differentiated by age, sex, education, life style, geography and so on. Because each group has strong preferences and consumer characteristics that can be easily reached through increasingly targeted communication and distribution channels. Most of marketers’ strategic decision making heavily depend on the demographic variables of people in the region where they focus on marketing their products. This study makes known the vital demographic structure of premium car owners in Chennai city and provides models for predicting the consumer’s decision to buy a car when his exact demographic profile is known. The relationship established between the demographic variables and the different stages of consumer’s purchase decision process further helps identifying the significant demographic variables. This will be definitely helpful to the marketers of cars to know their target group and to evolve marketing strategies to make them becoming a car owner.
A detailed geological history of quartz and industrial minerals present in different localities of
Eritrea is given. Well-grown transparent quartz crystals reflecting the hexagonal crystallographic features and
isolated, irregular shaped small milky quartz stones are found in western suburb of Asmara and the area
between Molebso and Zara in central northern Eritrea. Mechanism of formation of growth features observed on
the habit faces of transparent quartz crystals is briefly explained. Micro-topographical studies carried out on
these crystals indicate that to begin with, they grow and develop under high supersaturating conditions.
Most of the milky quartz stones are observed to be generally randomly scattered and devoid of gold. However,
few such specimens having yellow colored dots on their surfaces contain gold particles. Energy dispersion of Xray
analysis (EDAX) indicates high content of gold to the tune of 48% present in such samples. Commercial
implications related to quartz bearing gold are discussed. It is proposed that gold exists in large quantity in
quartz veins deep beneath the surface of earth in this region.
Simulation of the Linear Boltzmann Transport Equation in Modelling Of Photon ...IOSR Journals
A beam data modelling algorithm was developed by solving the linear Boltzmann Transport Equation (BTE). The Linear Boltzmann Transport Equation (LBTE) is a form of the Boltzmann transport equation that assumes that radiation particles only interact with the matter as they are passing through matter and not with each other. This condition is only valid when there is no external magnetic field. The numerical method proposed by Lewis et al., [9] was used to solve the LBTE. A programming code was computed for the LBTE and run on CMS XiO treatment planning system to generate beam data, the generated beam data were compared to experimentally determined data. The calculated percentage depth dose (PDD) completely overlap the measured PDDs for the small field sizes while there is a shift in the PDD tail for large field size. However the shift is negligible. For the wedge PDDs, the shift between the measured PDDs and the calculated occurs at the Dmax region and it increases with increase in field size. The calculated wedge profiles have a slight shift at the shoulder compared to the measured ones and this decreases with increase in field size, unlike the PDDs. There is also a slight shift between calculated in-plane profiles and measured ones. There is a good agreement between the measured beam data and the calculated ones using the algorithm. This algorithm can be implemented as an in-house algorithm for beam data modelling and also as an independent quality assurance tool for checking the accuracy of clinical TPS algorithms with regards to beam data modelling during quality assurance and TPS commissioning tests.
On The Use of Transportation Techniques to Determine the Cost of Transporting...IOSR Journals
This paper aims at identifying an effective and appropriate method of calculating the cost of transporting goods from several supply centers to several demand centers out of many available methods. Transportation algorithms of North-West corner method (NWCM), Least Cost Method (LCM), Vogel’s Approximation Method (VAM) and Optimality Test were carried out to estimate the cost of transporting produced newspaper from production center to ware-houses using Statistical software called TORA. The results revealed that: NWCM = 36,689,050.00, LCM = 55,250,034.00, VAM = 29,097,700.00 and Optimal solution = 19,566,332.00. It was discovered that Vogel’s Approximation method gives the transportation cost that closer to optimal solution. Also, the study revealed that a production center should be created at northern part of Nigeria to replace the dummy supply center used in the analysis, so as to make production capacity equal to requirement.
Stable endemic malaria in a rainforest community of Southeastern NigeriaIOSR Journals
Malaria infections in a stable endemic malaria community of Abagana, a rainforest community in
southeastern Nigeria was studied between April and August 2012. Advocacy visits to the traditional ruler and
opinion leaders of the community and proper explanations of the project were used to obtain permission to
carry out the study. The community was mobilized through public announcements in the churches, schools,
markets and group meetings. Thick and thin blood films were used to concentrate, and identify malaria
parasites using oil immersion lense of bright field light microscope. Estimates of parasite intensity per person
was made on each positive slide by parasite count in the microscope fields. Participants were grouped into
sexes, age, education and occupation. A total of 141 participants made up of 59(41.84%) males and 82(58.16%)
females were involved in the study. Of the 141 participants, 76(53.90%) were positive with malaria parasites,
among whom 32(42.11%) were males and 44(57.89%) were females. Of the positive malaria cases, malaria
intensity among the participants were light 32(42.11%), moderate 35(46.05%) and heavy 9(11.84%) and was
spread across all the groups and villages. These results revealed holoendemicity of malaria in the community.
Intervention efforts including massive educational campaigns were suggested
Design and Analysis of Microstrip Antenna for CDMA Systems CommunicationIOSR Journals
This paper proposes a newly designed microstrip patch antennas (MSA) for wireless application
(CDMA Systems). The designed single antenna E-shaped patch antenna. Two parallel slots are in corporated
into the patch of a microstrip antenna to expand it bandwidth, and designed antenna operates in the frequency
range of 1.85 to 1.99 GHz. The antenna is designed using air as a dielectric substrate between the ground plane
and substrate patch antenna. IE3D is a full-wave electromagnetic simulator based on the method of moments
(MoM) technique. It has been widely used in the design of MICs, RFICs, patch antennas, wire antennas, and
other RF/wireless antennas. It can be used to calculate and plot the S parameters, VSWR, current distributions
as well as the radiation patterns. The results obtained for each patch were 2D and 3D view of patch, Directivity,
Gain, beam width and other such parameters, true and mapped 3D radiation pattern, and 2D polar radiation
pattern. The antenna successfully achieves the exhibit a broad impedance bandwidth of 27 % (at VSWR < 2)
with respect to the center frequency of 1.9 GHz is designed, fabricated, and finally measured on Spectrum
analyzer. The radiation pattern and directivity are also presented.. Gain maximum achievable is 3 dBi and good
return loss (S11 parameters) of -30 dB is achieved along with broadside radiation pattern.
Comparative Analysis of the Different Brassica OleraceaVarieties Grown on Jos...IOSR Journals
This study was carried out to determine and compare the phytochemical, anti-nutrients, proximate composition and the effects of Brassica oleracea varieties on hepatic and erythropoietic parameters such as liver enzymes and packed cell volume (PCV) respectively. Fresh samples of the different varieties of Brassica oleracea namely: Brassica oleracearepa(Chinese cabbage), Brassica oleracearupetris(red cabbage) and Brassica oleraceapeviridis(green cabbage) were collected from Kasa in Plateau state, Nigeria, and were identified. After the authentication of these samples, the effect of gastric inturbation (oral administration) of the aqueous extracts on Male White Albino rats was observed for 14days. Each of the three (3) varieties were analysed for proximate composition, phytochemicals and anti-nutrients. It was observed that Brassica olereceais an important source of nutrients, particularly minerals. However, the high content of anti-nutritional factors such as cyanides, tannins, oxalates and phytic acids make these minerals bio-unavailable due to the process of chelation. It was also observed that the 3 varieties could have possible effects in the reduction of packed cell volume (PCV)/ Haemoglobin (Hb) levels and in the elevation of liver enzymes activity (Alkaline phosphate, ALT and AST). One could therefore conclude that there is a change in PCV/Hb levels and liver enzymes activity of extract-fed subjects from Brassica oleraceavarieties to the control subjects from normal diet
Magnetic Properties and Interactions of Nanostructured CoCrTa Thin FilmsIOSR Journals
Magnetic properties of CoCrTa alloy thin films were studied as function of the deposition pressure. Films deposited at low deposition pressure showed low coercivity and high loop squareness ratio. At relatively higher deposition pressurean increase in the samples’ coercivity, and decrease in both the magnetic loop squareness ratio, andthe strength of the exchange interaction amongst the grains of the films were recorded. The observations indicate the films to have properties quite suited for recording media application as well as magnetic memory devices.
Potential Biodeteriogens of Indoor and Outdoor Surfaces (Coated With Gloss, E...IOSR Journals
Potential Biodeteriogens of indoor and outdoor surfaces (coated with gloss, emulsion and text coat paints) within the University of Port Harcourt, Nigeria were investigated. Potential Biodeteriogens implicated in deterioration of painted surfaces were bacteria, fungi, microalgae and cyanobacteria. The total heterotrophic bacteria counts and total fungal counts for outdoor and indoor painted surfaces ranged from 2.8 x 106 to 9.00 x 106 cfu/g paint scrape, 1.56 x 104 to 6.6 x 104 cfu/g paint scrape; and 1.1 x 106 to 6.5 x 106 cfu/g paint scrapes, 1.31 x 104 to 9.8 x 104 cfu/g paint scrapes respectively. The result of THB and TF count expressed graphically showed surfaces with increasing order of microbial load: Gloss paints < Text coat paint < Emulsion paints. Predominant bacterial genera isolated from the surfaces include Bacillus (29.0%), Pseudomonas (22.6%), Proteus (19.4%), Serratia (16.1%), Citrobacter (6.5%), Enterobacter (3.2%) and Klebsiella (3.2%). Fungal genera isolated include: Alternaria, Aspergillus , Cladosporium , Fusarium ,Geotrichum , Gleosporium , Penicillum , Rhizopus , Saccharomyces and Stachybotrys . Fungi were the predominant biodeteriogens. Predominant microalgae isolated from the wet painted surfaces include Chorella , Characium , Closterium , Geminella , Oscillatoria , Totrogonnidium and Triceratium . Physicochemistry of various paint surfaces revealed the following: TOC (1.30 – 3.49%), Phosphate (0.39-8.82mg/100g), nitrate (4.64-187.58mg/100g), sulphate (99.78-285.00mg/100g), pH (8.55-9.59), oil and Grease (125.00-285.00mg/100g).Result showed that different consortia of biodeteriogens implicated in indoor and outdoor painted surfaces are dependent on the chemical compositions of the various paints, nature of the coating surfaces and physicochemical parameters influencing the microbial processes. Emulsion surfaces habour most potential biodeteriogens on their surfaces than the other surfaces. There were significant differences (P < 0.05) in the various potential biodeteriogens, categories of painted surfaces, indoor and outdoor surfaces.
Corporate Governance, Firm Size, and Earning Management: Evidence in Indonesi...IOSR Journals
Purpose –Thepurpose of this paper is to evaluate the impact of the corporate governance regulationsimplementation and firm size onthe earning management for food and beverages companies in Indonesian Stock Exchange. Design/methodology/approach –The multiple regression is utilized to test this relationship at 95% confidence.Corporate governance was proxied by board of director, audit quality, and board independence. Firm size was represented by natural logarithm of total assets. Earning management was measured by Jones model withdiscretionary accruals. Findings – Using data from the year 2005 annual reports of 51 food and beverages listed companies,including the composite index, the results showed that twoof the corporate governance variables, namely board of director and audit quality, as well as firm size are statistically significant in explaining earning management measured bydiscretionary accruals. Research limitations/implications – The regulations on corporate governance were implementedin 2005, but not all of food and beverages listed companies implemented the regulations in 2005. Practical implications – An implication of this finding is that regulatory efforts initiated after the1997 financial crisis to enhance corporate transparency and accountability did not appear to result on better corporate performance. Originality/value – This is one of the few studies which investigates the impact of regulatory actionson corporate governance on earning management immediately after its implementation.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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International Journal of Engineering Research and Development (IJERD)IJERD Editor
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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 Corpus Driven, Aspect-based Sentiment Analysis To Evaluate In Almost Real-t...CSCJournals
Nowadays, more than ever, customers have access to other consumers’ digital evaluations concerning the products or services that they have consumed. The use of online review websites, by the potential digital consumers, makes them aware of the choices they have. This, enables them to make comparisons between all the available products or services. However, the big volume of the opinionative data that is produced continuously, creates difficulties when being analyzed by stakeholders, mostly due to human’s physical or mental restrictions. In this research, web scraping combined with an aspect-level sentiment analysis using the corpus-based technique, approached methodologically the problem, by identifying not only the relevant information, but also the particular expressions and phrases that the reviewers use over the Internet. The purpose is to recommend a corpus-based, sentiment analysis web system for detecting and quantifying customers’ opinions which are written in Greek language and referred to the Food and Beverage (F&B) sector in almost real-time. The system consists of two modules that constructed using the aforementioned methods. As far as the web scraping module is concerned, the BeautifulSoup and the Requests libraries of Python programming language were used. For the constructing purposes of the corpus-based sentiment analysis module, 80,500 customers’ reviews are extracted (data set) from 6,795 companies which selected randomly from the most popular Greek e-ordering platform. The evaluated functions are the quality of food, the customer service and the image of the company. The extracted sentiment orientation terms and phrases from the customers’ reviews are used to form the corresponding dictionaries of the functions and the appropriate pattern of tags, in order to proceed in the sentiment classification. Finally, the system is tested in the dataset and the findings will be practical and significant, as not enough attention has been paid in sentiment analysis techniques used in combination with a non-English, like the modern Greek language.
SENTIMENT ANALYSIS ON PRODUCT FEATURES BASED ON LEXICON APPROACH USING NATURA...ijnlc
Sentiment analysis has played an important role in identifying what other people think and what their behavior is. Text can be used to analyze the sentiment and classified as positive, negative or neutral. Applying the sentiment analysis on the product reviews on e-market helps not only the customer but also the industry people for taking decision. The method which provides sentiment analysis about the individual product’s features is discussed here. This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
SENTIMENT ANALYSIS ON PRODUCT FEATURES BASED ON LEXICON APPROACH USING NATURA...kevig
This paper presents the use of Natural Language Processing and SentiWordNet in this interesting application in Python: 1. Sentiment Analysis on Product review [Domain: Electronic]2. sentiment analysis regarding the product’s feature present in the product review [Sub Domain: Mobile Phones]. It usesa lexicon based approach in which text is tokenized for calculating the sentiment analysis of the product reviews on a e-market. The first part of paper includessentiment analyzer whichclassifiesthe sentiment present in product reviews into positive, negative or neutral depending on the polarity. The second part of the paper is an extension to the first part in which the customer review’s containing product’s features will be segregated and then these separated reviews are classified into positive, negative and neutral using sentiment analysis. Here, mobile phones are used as the product with features as screen, processors, etc. This gives a business solution for users and industries for effective product decisions.
The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to
sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available
in digital form. One important problem in sentiment analysis of product reviews is to produce summary of
opinions based on product features. We have surveyed and analyzed in this paper, various techniques that
have been developed for the key tasks of opinion mining. We have provided an overall picture of what is
involved in developing a software system for opinion mining on the basis of our survey and analysis.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 1, Ver. III (Jan – Feb. 2016), PP 50-54
www.iosrjournals.org
DOI: 10.9790/0661-18135054 www.iosrjournals.org 50 | Page
Sentiment Mining and Related Classifiers: A Review
Rehee Mehta1
, Dr. Shaily Jain2
1
(Computer Science Department, Chitkara University, India)
2
(Computer Science Department, Chitkara University, India)
Abstract: Brokers trading goods on the Web often seek for their customer’s reviews and feedbacks of their
products and the associated services. With the growing popularity of e-commerce, the number of customer
opinions that a product receives expands rapidly. This makes it difficult for both the customers as well as the
manufacturers to extract an accurate decision or outcome regarding the product’s quality.
This problem can be dealt easily by mining the characteristics of the product on which the customers have
expressed their feedbacks and reviews. This paper covers all the fundamental details about opinion analysis. It
comprises of current research and forthcoming scope of sentiment mining. Also the information regarding basic
workflow of the opinion mining process, recent trends, and applications of sentiment analysis has been
explained extraordinarily.
Keywords : Data Mining, Opinion Mining, surveys, reviews, Sentiment Analysis, Data Pre-processing,
Opinions.
I. Introduction
Data mining is a computer aided process for discovering patterns in large data sets. The overall aim of
data mining process is to gather information from a data set and convert it into an understandable form for future
use. Web Mining is one of the applications of data mining techniques to discover patterns from the Web.
According to the objective of the analysis, web mining can be divided into three broad categories: Web usage
mining, Web content mining and Web structure mining. Web Content Mining is the process to fetch useful
information from text, image, audio or video data in the web. It can also be referred as web text mining as the
text content is the most widely researched area. Opinion mining is a sub discipline of web content mining which
is also called as sentiment analysis. It is a process of finding users opinion about particular topic or a product. It
aims to make a computer machine capable of understanding human emotions and sentiments in a way a human
could understand and respond accordingly [6,8].
Generally before purchasing or launching a new product in the market, companies or individuals
excavate different opinions from several people. Depending upon these opinions one can decide to continue in
the same direction or to give it a second thought. Such surveys prove to be extremely beneficial economically
and practically. Earlier such surveys were conducted manually by distributing pamphlets, collecting information
from a sample of individuals, paper-and-pencil interviewing, face-to-face surveys, telephone surveys, mail
surveys etc. But from past several years web has dramatically changed the way to express opinions by posting
on merchant sites, review portals, blogs, Internet forums and much more. Such type of data is usually referred to
as user-generated content or user-generated media. Various review-related websites, businesses, government
intelligence applications and several other domains are quite curious about this online ‘word-of-mouth’, as it
provides them information about their customer’s choices and demands, as well as the positive and negative
comments on their products, thus giving them insight of their product’s flaws and an edge over their
competitors. It also provides the customers with useful and appropriate knowledge about the products and
services to aid in their purchase decision making process.
This paper discusses the existing works on opinion mining and sentimental analysis of customer
assessments and reviews online. With the progression of web technology, there is a large amount of data present
in the web for the users. These users not only use the existing resources in the web, but also give their
effective feedbacks, thus adding up useful information. Due to big amount of user’s opinions, feedbacks and
suggestions presented by the web resources, it is very much necessary to analyze and systemize their reviews for
better decision making. Opinion Mining is an Information Extraction and Natural Language Processing task that
classifies the user’s comments in the form of positive, negative or neutral categories. There are numerous
supervised or data-driven techniques for analyzing the sentiments of the users such as Naive Byes, Maximum
Entropy and SVM.
2. Sentiment Mining And Related Classifiers: A Review
DOI: 10.9790/0661-18135054 www.iosrjournals.org 51 | Page
Figure 1 Data Mining and Its Types
II. Related Work
From past few years businesses and research organizations have started focusing on social media and
big data. However, the field of supply chain management (SCM) has been comparatively behind in the area of
research and practice. Related to this, a research contributing to the SCM community presented a novel,
analytical framework (Twitter Analytics) for evaluating supply chain tweets, featuring the current use of Twitter
in supply chain context. Thus, observing the possible role of Twitter for supply chain practice and research. The
suggested framework associates three methodologies – content analytics (CA) , descriptive analytics (DA) and
network analytics (NA) relying upon network visualization and metrics for extracting knowledge from 22,399
#supply chain tweets. The outcome of the paper involved the supply chain tweets used by distinct groups of
supply chain professionals and organizations such as news services, IT industries, logistic providers,
manufacturers, etc for data sharing, appointing professionals communicating with stakeholders etc. Also several
other topics such as logistics and corporate social responsibility to uncertainty, manufacturing, SCMIT and even
human rights were examined [1]. The social media collects the data in structured and unstructured, formal and
informal form as the users do not care about the spellings and grammatical formation of a sentence while
communicating with each other using different social networking websites such as Facebook, orkut, LinkedIn,
instagram, etc. The collected data consisted of sentiments and opinions of users which were processed using
data mining techniques and were analyzed for capturing the useful information from it [3].
An opinion mining extraction algorithm to jointly explore the essential opinion mining elements was
proposed. Particularly, the algorithm automatically creates kernels to join closely related words into new terms
from word level to phrase level based on dependency relations and assured the certainty of opinion expressions
and polarity based on fuzzy dimensions, opinion rate intensifiers, and opinion patterns. Some interesting
observations were acknowledged like the negative polarity of video dimension was greater than the product
usability dimension for a product. Still, increasing the dimension of product usability could effectively improve
the product [4]. The information on current trends, applications of opinion mining, several areas where it could
have been used and also lot of meaningful information on the recent research work that was being carried out in
this field of data mining was provided. Also, the primitive work plan of the sentiment analysis process, the
challenges and the forthcoming research being planned in the area of sentiment analysis was explained
remarkably [5]. A mining approach to mine and gather product characteristics, reviews from various web
sources for a particular product in which a rule-based approach system was implemented, was proposed, which
practiced linguistic and opinion mining of texts to mine feature-sentiment pairs that have sentence-level co-
occurrence in consumer feedback records. The captured feature-sentiment pairs were modeled, classified,
distinguished between formal, informal and undefined opinions [6]. A novel approach for contextualizing and
enriching massive semantic knowledge bases for sentiment analysis with a focus on Web intelligence platforms
and other highly efficient big data applications was presented. The method was not only relevant to traditional
sentiment lexicons, but also to broader, complete, multi-dimensional affective resources such as SenticNet [7].
The tool of Opinion mining and Sentiment Analysis processes a set of search results for a given item based on
the quality and characteristics. By analyzing customer review one can scale a particular product and provide
opinions for it. Research has been carried out in this field to mine opinions in the form of document, sentence
and feature level sentiment analysis. It is examined that now the opinion mining trend is shifting to the
3. Sentiment Mining And Related Classifiers: A Review
DOI: 10.9790/0661-18135054 www.iosrjournals.org 52 | Page
sentimental analysis of the data obtained from several social media websites such as twitter data, comments used
in Facebook on pictures, videos or Facebook status etc. Various techniques and tools of Opinion Mining were
discussed in this paper [8].
Opinion Mining/ Sentiment Analysis
Preprocessing
In this step, the raw data is collected and pre-processed for feature extraction. The pre-processing phase
can further be divided into number of sub phases which are as follows: In Tokenization phase, a text document
consisting of number of sentences is broken down into terms or tokens by removing white spaces, commas and
other symbols. Stop word removal discard the articles such as a, an, the. Stemming reduces relevant tokens into
a single term. Case Normalization is a method that has English texts to be written in both upper and lowercase
characters and converts the entire document into lowercase or uppercase.
Feature Extraction
The feature extraction phase deals with feature/characteristic types (which describes the type of
features used for sentiment analysis), feature selection (used to select appropriate features for sentiment
categorization), feature weighting mechanism (weights each characteristic for better recommendation) reduction
mechanisms (features for enhancing the classification process).
Feature Types
Types of features involved in opinion mining process are as follows:
i. Term frequency (number of time the term existed in a given document).
ii. Term co-occurrence (characteristics which exist together like unigram, bigram, trigram, etc).
iii. Part of speech information (POS tagger is used to isolate POS tokens).
iv. Opinion words (Opinion words are the terms which express positive (good) or negative (bad) sentiments).
v. Negations ((such as not, not only, etc) alter opinion orientation in a sentence) and
vi. Syntactic dependency (expressed in terms of a parse tree and consists of word dependency based features).
Feature Selection
i. Information gain (depending upon the presence and absence of a word in a given document, a threshold is
set and the words with low information gain are discarded).
ii. Odd Ratio (applicable for binary class domain where it has one positive and one negative class for
categorization.
iii. Document Frequency calculates the maximum number of occurrences of a term in the existing document
and based on the calculated threshold, the terms are discarded.
Features weighting mechanism
The mechanisms are of two types which are as follows:
i. Term Presence and Term Frequency- word which appears infrequently contains more information than
regularly occurring words.
ii. Term frequency and inverse document frequency (TFIDF) - Documents are ranked where highest grading is
given for words that occur frequently in a few documents and lowest grading for words that occur
frequently in every document.
COMPARASON AMONGST VARIOUS CLASSIFIERS
Table 1 Comparison Between Various Classifiers Based On Their Advantages And Disadvantages.
CLASSIFIER ADVANTAGES DISADVANTAGES
Naive Bayes
Classifier
Easy to implement.
Excellent computational efficiency & classification rate
Predict accurate results for most of the problems.
Precision decreases if the amount of data is less.
Large number of records required for good results.
Decision Tree Easy to understand.
Easy to generate rules.
Reduce problem complexity.
Training time is relatively expensive.
One branch
Once a mistake is made at a higher level, any sub
tree is wrong.
Does not handle continuous variable well.
May suffer from over fitting.
4. Sentiment Mining And Related Classifiers: A Review
DOI: 10.9790/0661-18135054 www.iosrjournals.org 53 | Page
K-nearest
neighbor
Effective
Non-parametric
Classes need not be linearly separable.
Zero cost of the learning process.
Robust to noisy training data.
Suitable for multimodal classes.
Classification time is high
Difficult to find optimal value of k.
High time consumption for large training data set.
Sensitive to noisy and irrelevant attribute.
Support Vector
Machine
Gathers the inherent features of the data better.
High accurate
Parameter tuning
kernel selection
Artificial Neural
Network
Algorithm
Easy to use
Reprogramming is not required
Easy implementation
Can be applied on number of problems
In case of large neural network, high processing time
is required
Number of neurons and layers are difficult to
compute
Slow learning
Application Areas
Sentiment Analysis And Opinion Mining Covers A Broad Range Of Applications
Some of which are as follows:
i. Commercial markets: For the business investors it is important to analyze the market trends and other
investor’s opinions about the stocks of a company, to identify price trends.
ii. Goods or commodities: An organization is curious in customers' reviews and feedbacks about its products.
Information may be used to enhance the product’s quality and recognizing new marketing strategies.
iii. Maps or Location: Tourists are fascinated in gathering information about the best places to visit. Thus
opinion mining can be used for this purpose for capturing relevant information before planning a trip.
iv. Analysis of software programs: We can detect users' sentiments from posted reviews on specialized sites.
v. Voting Suggestion Applications: It assists the voters in perceiving which political party has closer positions
to their choice. For example, SmartVote.ch asks the voter to establish its degree of agreement with a
number of policy statements, and then correlates its position with the political parties.
vi. Computerized content analysis: It assists in processing huge amount of qualitative data. Nowadays there are
numerous tools in the market that combine statistical algorithm with semantics and machine learning with
human instructions. These results are able to analyze relevant comments and assign positive or negative
implications to it (also called as sentiment).
Why It Matters In Governance
Opinion mining applications are the base of large scale collaborative guidelines. It helps to identify
initial cautionary system of possible interruption in an appropriate manner, by detecting early reviews from
inhabitants. Generally, impromptu surveys are followed to collect feedbacks in a systematic manner. However,
this type of data assemblage is not economical, as it requires expenditure in design and data collection; it is quite
hard, as people are not responsive in acknowledging surveys and hence it is not profitable, as it detects known
issues through pre-existing questions and respondents, but is unsuccessful to detect the most significant
problems, the famous ‘unknown unknown’. Sentiment analysis aids to detect problems by listening and not by
asking, thus assuring a more factual impression of current scenario. Argument mapping application is effective
to assure that policy arguments are logical and proof-based, and do not carry out the same debate repeatedly.
Such software would ultimately be useful for policy-makers as well as for citizens who could more easily grasp
the essential points of a discussion and involve in the policy-making criteria.
III. Conclusion
Sentiment analysis emerges as a challenging field with several barriers. It has diverse applications that
could turn out to be extremely beneficial in number of fields such as manufacturing, business analytics,
marketing, etc. This study provides a comprehensive understanding of various opinion mining classifiers. From
our review we concluded that different algorithms execute differently depending on the data accumulation. In
this paper we have examined different classifiers with their pros and cons. Some of these algorithms perform
fairly while some perform extraordinarily depending upon the requirements of the user. None of them appears to
be exceptionally superior over the others in all contexts.
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