Contextual semantics for sentiment analysis of Twitter

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Saif, Hassan ; He, Yulan ; Fernández, Miriam ; Alani, Harith (2016)

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.
  • References (39)
    39 references, page 1 of 4

    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R., 2011. Sentiment analysis of twitter data. In: Proc. ACL 2011 Workshop on Languages in Social Media. Portland, Oregon.

    Aue, A., Gamon, M., 2005. Customizing sentiment classifiers to new domains: A case study. In: Proceedings of recent advances in natural language processing (RANLP). Borovets, Bulgaria.

    Baccianella, S., Esuli, A., Sebastiani, F., 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Seventh conference on International Language Resources and Evaluation, Malta. Retrieved May. Valletta, Malta.

    Barbosa, L., Feng, J., 2010. Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of COLING. Beijing, China.

    Batra, S., Rao, D., 2010. Entity based sentiment analysis on twitter. Science, 1-12.

    Bifet, A., Frank, E., 2010. Sentiment knowledge discovery in twitter streaming data. In: Discovery Science. Canberra, Australia.

    Cambria, E., 2013. An introduction to concept-level sentiment analysis. In: Advances in Soft Computing and Its Applications. Springer, pp. 478-483.

    Cambria, E., Havasi, C., Hussain, A., 2012. Senticnet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: FLAIRS Conference. pp. 202-207.

    Diakopoulos, N., Shamma, D., 2010. Characterizing debate performance via aggregated twitter sentiment. In: Proc. 28th Int. Conf. on Human factors in computing systems. ACM.

    Ding, X., Liu, B., Yu, P. S., 2008. A holistic lexicon-based approach to opinion mining. In: Proceedings of the international conference on Web search and web data mining. Palo Alto, California, USA.

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