Author : Veerappa B. Pagi 1
Date of Publication :22nd March 2019
Abstract: With the advent of web 2.0 and related technologies, the volume of user-generated social media content (UGSMC) is rapidly growing and likely to increase even more in the near future. Social networking apps such as Twitter, Facebook and Google+ are gaining more popularity as they allow people to share and express their views about happenings, have discussion with different communities, or post messages across the world. Twitter sentiment analysis (TSA) extends any organization’s ability to capture and study public sentiment towards the social events and commodities related to them in real time. This paper provides a comprehensive survey on techniques and applications of TSA available in the literature. The survey focuses on issues such as pre-processing techniques, feature selection methods, learning models, and performance of each method as a criterion. The survey reveals some of the traditional machine learning (ML) algorithms have been efficiently used to work on Twitter data. In conclusion, the paper cites many promising issues for further research in this domain.
Reference :
-
- Min-Chul Yang and Hae-Chang Rim, „Identifying interesting Twitter contents using topical analysis‟, Expert Systems with Applications, Expert Systems with Applications, 41 (2014) 4330–4336
- Zhao Jianqiang and Gui Xiaolin, Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis, IEEE Access, 5 (2017), 2870-2879.
- Hyeok-Jun Choi and Cheong Hee Park, Emerging Topic Detection in Twitter Stream based on High Utility Pattern Mining, Expert Systems With Applications, (2018), doi:10.1016/j.eswa.2018.07.051.
- Ortigosa, A., et al. Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior (2013), http://dx.doi.org/10.1016/j.chb.2013.05.024.
- Feldman, R. and Dagan, I. (1995) „Knowledge discovery in textual databases‟, 1st International Conference on Knowledge Discovery and Data Mining, pp.112–117.
- Salton, G. and Yang, C.S. (1975) „A vector space model for automatic indexing‟, Communications of the ACM, Vol. 18, No. 11, pp.613–620.
- Tsatsaronis, G., Varlamis, I. and Nørvg, K. (2010) „An experimental study on unsupervised graph-based word sense disambiguation‟, Computational Linguistics and Intelligent Text Processing, LNCS, Vol. 6008, pp.184– 198, Springer
- Zhang, X., Xu, C., Cheng, J., Lu, H. and Ma, S. (2009) „Effective annotation and search for video blogs with integration of context and content analysis‟, IEEE Transactions on Multimedia Special Issue on Integration of Context and Content, Vol. 11, No. 2.
- Porter, M.F. (2006) „An algorithm for suffix stripping‟, Electronic Library and Electronic Systems, Vol. 40, pp.211–218
- Pappas, N., Katsimpras, G. and Stamatatos, E. (2012) „Extracting informative textual parts from web pages containing user-generated content‟, Proceedings of 12th International Conference on Knowledge Management and Knowledge Technologies, No. 4, pp.1–8.
- Baccianella, S., Esuli, A. and Sebastiani, F. (2010) „Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining‟, Proceedings of the Annual Conference on Language Resources and Evaluation, pp.2200–2204.
- Turney, P. (2000) „Learning algorithms for keyphrase extraction‟, Information Retrieval, Vol. 2, pp.303–336.
- Fang, X. and Zhan, J. (2015) „Sentiment analysis using product review data‟, Journal of Big Data, Vol. 2, No. 5, Springer.
- Nadeau, D. and Sekine, S. (2007) „A survey of named entity recognition and classification‟, Lingvisticae Investigationes, pp.3–26.
- Pang, L., Zhu, S. and Ngo, C-W. (2015) „Deep multimodal learning for affective analysis and retrieval‟, IEEE Transactions on Multimedia, Vol. 17, No. 11, pp.2008–2020.
- Pröllochs, N., Feuerriegel, S. and Neumann, D. (2016) „Negation scope detection in sentiment analysis: decision support for news-driven trading‟, Decision Support Systems, Vol. 88, pp.67–75, Elsevier.
- Araujo, L. and Martinez-Romo, J. (2010) „Web spam detection new classification features based on qualified link analysis and language models‟, IEEE Transactions on Information Forensics and Security, Vol. 5, No. 3.
- Aas, K. and Eikvil, L. (1999) Text Categorization: A Survey, Technical report, Norwegian Computing Center.
- Liu, C-L., Hsaio, W-H., Lee, C-H. and Chi, H-C. (2013) „An HMM based algorithm for content ranking and coherence feature extraction‟, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 43, No. 2, pp.440–450.
- Forman, G. (2003) „An extensive empirical study of feature selection metrics for text classification‟, J. Mach. Learn. Res., Vol. 3, pp.1289–1305.
- Prieto, V.M., Álvarez, M., López-García, R. and Cacheda, F. (2012) „Analysis and detection of web spam by means of web content‟, IRFC‟12 Proceedings of the 5th conference on Multidisciplinary Information Retrieval, pp.43–57.
- Young, T.Y. (1971) „The reliability of linear feature extractors‟, IEEE Transactions on Computers, Vol. 20, No. 9, pp.967–971.
- Chimphlee, S., Salim, N., Ngadiman, M.S.B., Chimphlee, W. and Srinoy, S. (2006) „Independent component analysis and rough fuzzy based approach to web usage mining‟, Proceedings of the 24th International conference on Artificial intelligence and applications (IASTED), ACTA Press, Anaheim, pp.422–427.
- Taboada, M. et al. (2011) „Lexicon-based methods for sentiment analysis‟, Computational linguistics, Vol. 37, No. 2, pp.267–307.
- Russell, S. and Norvig, P. (2003[1995]) Artificial Intelligence: A Modern Approach, 2nd ed., Prentice Hall.
- M. Ghiassi a, J. Skinner b and D. Zimbra, Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network, Expert Systems with Applications 40 (2013) 6266–6282.
- Aibek Makazhanov, Davood Rafiei and Muhammad Waqar, Predicting political preference of Twitter users, Soc. Netw. Anal. Min. (2014) 4:193.
- Vadim Kagan and Andrew Stevens and V.S. Subrahmanian, Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election, IEEE Intelligent Systems (2015), 1-5.
- Mark E. Larsen, We Feel: Mapping emotion on Twitter, IEEE Journal of Biomedical and Health Informatics, (2015) 1246-1252.
- Van de Kauter, M., et al. Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Systems, with Applications (2015), http://dx.doi.org/10.1016/j.eswa.2015.02.007
- Xiang Ji et al., Twitter sentiment classification for measuring public health concerns, Soc. Netw. Anal. Min. (2015) 5:13.
- Mondher Bouazizi and Tomoaki Otsuki, A PatternBased Approach for Sarcasm Detection on Twitter, 4, 2016, 5477-5488.