A Sentiment Classification Approach of Sentences Clustering in Webcast Barrages
1
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Manyof the sentiment analysis and opinion mining applications focus on product reviews, social media reviews,forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze thesentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, andthere are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluativesentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solvethe problem of scattering the information for each barrage. The method developed in this paper contains twosubtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and removeunavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, toanalyze the sentiment of the barrage. According to our experimental results, this method shows that it performswell in analyzing the sentiment of online webcast barrages.
Abstract
Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Manyof the sentiment analysis and opinion mining applications focus on product reviews, social media reviews,forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze thesentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, andthere are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluativesentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solvethe problem of scattering the information for each barrage. The method developed in this paper contains twosubtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and removeunavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, toanalyze the sentiment of the barrage. According to our experimental results, this method shows that it performswell in analyzing the sentiment of online webcast barrages.
참고문헌 (35)
- B. Liu, "Sentiment Analysis: Mining Sentiments, Opinions, And Emotions", pp.2015
- K. Ravi, "A survey on opinion mining and sentiment analysis : tasks, approaches and applications", Knowledge-Based Systems, 89, pp.14~46 (2015)
- E. Cambria, "Affective computing and sentiment analysis", IEEE Intelligent Systems, 31(2), pp.102~107 (2016)
- E. Cambria, "Jumping NLP curves : a review of natural language processing research", IEEE Computational Intelligence Magazine, 9(2), pp.48~57 (2014)
- B. Pang, "Thumbs up? : sentiment classification using machine learning techniques", Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, pp.79~86 (2002)
- Z. Zhai, "Identifying evaluative sentences in online discussions", Proceedings of the 25th AAAI Conference on Artificial Intelligence, pp.933~938 (2011)
- B. Pang, "A sentimental education : Sentiment analysis using subjectivity summarization based on minimum cuts", Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp.271~278 (2004)
- B. Pang, "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval, 2(1-2), pp.1~135 (2008)
- M. Hu, "Mining and summarizing customer reviews", Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.168~177 (2004)
- L. Jiang, "Target-dependent twitter sentiment classification", Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics : Human Language Technologies, pp.151~160 (2011)
- L. Barbosa, "Robust sentiment detection on twitter from biased and noisy data", Proceedings of the 23rd International Conference on Computational Linguistics : Posters, pp.36~44 (2020)
- X. Hu, "Exploiting social relations for sentiment analysis in microblogging", Proceedings of the 6th ACM International Conference on Web Search and Data Mining, pp.537~546 (2013)
- L. Gong, "Modeling social norms evolution for personalized sentiment classification", Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1 : Long Papers), pp.855~865 (2016)
- Shi Dong, "Microblog Sentiment Analysis Method Based on Spectral Clustering", JIPS(Journal of Information Processing Systems), 14(3), pp.727~739 (2018), 한국정보처리학회[DOI]
- A. Hassan, "What's with the attitude? Identifying sentences with attitude in online discussions", Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp.1245~1255 (2010)
- M. Salehan, "Predicting the performance of online consumer reviews : a sentiment mining approach to big data analytics", Decision Support Systems, 81, pp.30~40 (2016)
- R. Socher, "Semi-supervised recursive autoencoders for predicting sentiment distributions", Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp.151~161 (2011)
- A. Ramesh, "Weakly supervised models of aspect-sentiment for online course discussion forums", Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1, pp.74~83 (2015)
- M. Fernandez-Gavilanes, "Unsupervised method for sentiment analysis in online texts", Expert Systems with Applications, 58, pp.57~75 (2016)
- J. Li, "Hierarchical classification in text mining for sentiment analysis of online news", Soft Computing, 20(9), pp.3411~3420 (2016)
- D. D. Wu, "A decision support approach for online stock forum sentiment analysis", IEEE Transactions on Systems, Man, and Cybernetics : Systems, 44(8), pp.1077~1087 (2014)
- Y. Liu, "Ranking products through online reviews : a method based on sentiment analysis technique and intuitionistic fuzzy set theory", Information Fusion, 36, pp.149~161 (2017)
- D. T. Vo, "Don’t count, predict! an automatic approach to learning sentiment lexicons for short text", Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2, pp.219~224 (2016)
- H. Saif, "Semantic sentiment analysis of twitter", The Semantic Web – ISWC 2012, pp.508~524 (2012)
- K. Schouten, "Survey on aspect-level sentiment analysis", IEEE Transactions on Knowledge and Data Engineering, 28(3), pp.813~830 (2015)
- A. Hussain, "Semi-supervised learning for big social data analysis", Neurocomputing, 275, pp.1662~1673 (2018)
- Y. Wu, "Flame : a probabilistic model combining aspect based opinion mining and collaborative filtering", Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp.199~208 (2015)
- Y. Wang, "Attention-based LSTM for aspect-level sentiment classification", Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.606~615 (2016)
- R. K. Amplayo, "Aspect sentiment model for micro reviews", Proceedings of 2017 IEEE International Conference on Data Mining(ICDM), pp.727~732 (2017)
- J. Feng, "Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm", Cluster Computing, 22(3), pp.5839~5857 (2019)
- L. Peng, "Feature selection and overlapping clustering-based multilabel classification model", Mathematical Problems in Engineering, 2018 (2018)
- N. W. Xue, "Chinese word segmentation as character tagging", Computational Linguistics and Chinese Language Processing, 8(1), pp.29~48 (2003)
- C. J. Hutto, "Vader : a parsimonious rule-based model for sentiment analysis of social media text", Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, pp.216~225 (2014)
- F. Pagani, "Measuring the role of greylisting and nolisting in fighting spam", Proceedings of 2016 46th Annual IEEE, 2016, 562~571
- W. Che, "Sentence compression for aspect-based sentiment analysis", IEEE, 2015, pp.23(12), 2111~2124