Abstract
Sentiment analysis, when combined with the vast amounts of data present in the social networking domain like Twitter data, becomes a powerful tool for opinion mining. In this paper we focus on identifying ‘the most influential sentiment’ for topics extracted from tweets using Latent Dirichlet Allocation (LDA) method. The most influential twitterers for various topics are identified using the TwitterRank algorithm. Then a SentiCircle based approach is used for capturing the dynamic context based entity level sentiment.
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Sheik, R., Philip, S.S., Sajeev, A., Sreenivasan, S., Jose, G. (2018). Entity Level Contextual Sentiment Detection of Topic Sensitive Influential Twitterers Using SentiCircles. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_19
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DOI: https://doi.org/10.1007/978-981-10-3223-3_19
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