Abstract
The Twitter platform is one of the most popular social media environments that gathers concise messages regarding the topics of the moment expressed by its users. Processing sentiments from tweets is a challenging task due to the natural language complexity, misspelling and short forms of words. The goal of this article is to present a hybrid feature for Twitter Sentiment Analysis, focused on information gathered from this social media. The baseline perspective is presented based on different scenarios that take into consideration preprocessing techniques, data representations, methods and evaluation measures. Also, several interesting features are detailed described: the hashtag-based, the fused one, and the raw text feature. All these perspectives are highlighted for proving the high importance and impact that the analysis of tweets has on social studies and society in general. We conducted several experiments that include all these features, with two granularity tweets (word and bigram) on Sanders dataset. The results reveal the idea that best polarity classification performances are produced by the fused feature, but overall the raw feature is better than other approaches. Therefore, the domain-specific features (in this case Twitter hashtags) represent information that can be an important factor for the polarity classification task, not exploited enough.
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References
Angiani, G., Ferrari, L., Fontanini, T., Fornacciari, P., Iotti, E., Magliani, F., Manicardi, S.: A comparison between preprocessing techniques for sentiment analysis in twitter. In: KDWeb. pp. 1–6 (2016)
Anjaria, M., Guddeti, R.M.R.: Influence factor based opinion mining of twitter data using supervised learning. In: 2014 Sixth COMSNETS. pp. 1–8. IEEE (2014)
Barhan, A., Shakhomirov, A.: Methods for sentiment analysis of twitter messages. In: 12th Conference of FRUCT Association. pp. 215–222 (2012)
Deshmukh, R., Pawar, K.: Twitter sentiment classification on Sanders data using hybrid approach. IOSR Journal of Computer Engineering 17, 118–123 (07 2015)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1(12), 2009 (2009)
Hamdan, H., Béchet, F., Bellot, P.: Experiments with dbpedia, wordnet and sentiwordnet as resources for sentiment analysis in micro-blogging. In: Proceedings of the 7th SemEval 2013. pp. 455–459 (2013)
Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: Fifth International AAAI conference on weblogs and social media. pp. 538–541 (2011)
Martınez-Cámara, E., et. al: Ensemble classifier for twitter sentiment analysis. In: NLP Applications: completing the puzzle. pp. 1–12 (2015)
Mitchell, R., Michalski, J., Carbonell, T.: An artificial intelligence approach. Springer (2013)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. LREc. 10, 1320–1326 (2010)
Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of twitter. In: International semantic web conference. pp. 508–524. Springer (2012)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information processing & management 24(5), 513–523 (1988)
Sankaranarayanan, J., et al.: Twitterstand: News in tweets. In: Proceedings of the 17th ACM SIGSPATIAL. pp. 42–51. GIS ’09, ACM (2009)
Severyn, A., Moschitti, A.: Unitn: Training deep CNN for twitter sentiment classification. In: Proceedings of the 9th SemEval 2015. pp. 464–469 (2015)
Tang, D., et al.: Coooolll: A deep learning system for twitter sentiment classification. In: Proceedings of the 8th SemEval 2014. pp. 208–212 (2014)
Wang, P., et al.: Semantic expansion using word embedding clustering and CNN for improving short text classification. Neurocomputing 174, 806–814 (2016)
Yan, Leiming, Zheng, Yuhui, Cao, Jie: Few-shot learning for short text classification. Multimedia Tools and Applications 77(22), 29799–29810 (2018). https://doi.org/10.1007/s11042-018-5772-4
Zhang, L., et al.: Combining lexicon-based and learning-based methods for twitter sentiment analysis. HP Laboratories, Technical Report HPL-2011 (2011)
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Limboi, S., Dioşan, L. (2020). Hybrid Features for Twitter Sentiment Analysis. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_19
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