The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the...The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.展开更多
Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention ...Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification.展开更多
In the current era of social media,different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events,campaigns,and election...In the current era of social media,different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events,campaigns,and elections.The acquisition,analysis,and presentation of such content have received considerable attention from opinion-mining researchers.For this purpose,different supervised and unsupervised techniques have been used.However,they have produced less efficient results,which need to be improved by incorporating additional classifiers with the extended data sets.The authors investigate different su-pervised machine learning classifiers for classifying the political affiliations of users.For this purpose,a data set of political reviews is acquired from Twitter and annotated with different polarity classes.After pre-processing,different machine learning classifiers like K-nearest neighbor,naïve Bayes,support vector machine,extreme gradient boosting,and others,are applied.Experimental results illustrate that support vector machine and extreme gradient boosting have shown promising results for predicting political affiliations.展开更多
文摘The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.
基金This work has partially been sponsored by the Hungarian National Scientific Fund under contract OTKA 129374the Research&Development Operational Program for the project“Modernization and Improvement of Technical Infrastructure for Research and Development of J.Selye University in the Fields of Nanotechnology and Intelligent Space”,ITMS 26210120042,co-funded by the European Regional Development Fund.
文摘Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification.
文摘In the current era of social media,different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events,campaigns,and elections.The acquisition,analysis,and presentation of such content have received considerable attention from opinion-mining researchers.For this purpose,different supervised and unsupervised techniques have been used.However,they have produced less efficient results,which need to be improved by incorporating additional classifiers with the extended data sets.The authors investigate different su-pervised machine learning classifiers for classifying the political affiliations of users.For this purpose,a data set of political reviews is acquired from Twitter and annotated with different polarity classes.After pre-processing,different machine learning classifiers like K-nearest neighbor,naïve Bayes,support vector machine,extreme gradient boosting,and others,are applied.Experimental results illustrate that support vector machine and extreme gradient boosting have shown promising results for predicting political affiliations.
