Abstract
Social networking platforms such as Facebook and Twitter have become a very popular communication tools among online users to share and express opinions and sentiment about the surrounding world. The availability of such opinionated text content has drawn much attention in the field of Natural Language Processing. Compared to other languages, such as English, little work has been done for Indian languages in this domain. In this paper, we present our contribution in classifying sentiment polarity for Indian tweets as a part of the shared task on Sentiment Analysis in Indian Languages (SAIL 2015). With the support of a distributional thesaurus (DTs) and sentence level co-occurrences, we expand existing Indian sentiment lexicons to reach a higher coverage on sentiment words. Our system achieves an accuracy of 43.20 % and 49.68 % for the constrained submission, and an accuracy of 42.0 % and 46.25 % for the unconstrained setup for Bengali and Hindi, respectively. This puts our system in the first position for Bengali and in the third position for Hindi, amongst six participating teams.
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Kumar, A., Kohail, S., Ekbal, A., Biemann, C. (2015). IIT-TUDA: System for Sentiment Analysis in Indian Languages Using Lexical Acquisition. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_65
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