Fajri Koto
ABSTRACT
In this paper we report the first effort of constructing emotion lexicon by utilizing Twitter as source of data. Specifically we used hashtag feature to obtain tweets with certain emotion label in English. There are eight emotion classes used in our work, comprising of angry, disgust, fear, joy, sad, surprise, trust and anticipation that refer to the Plutchik's wheel. To obtain the lexicon, we first ranked the words according to its term frequency. After that, we reduced some irrelevant words by removing words with low frequency. We also enriched the lexicon with the synonym and conducted filtering by utilizing sentiment lexicon (40,288 words). As result, we successfully constructed 4 Hashtag-Based Emotion (HBE) Lexicons through different procedures and called them as HBE-A1 (50,613 words), HBE-B1 (23,400 words), HBE-A2 (26,909 words) and HBE-B2 (14,905 words). In our experiment, we used the lexicons in investigating Twitter Sentiment Analysis and the result reveals that our proposed emotion lexicons can boost the accuracy and even improve over than NRC-Emotion lexicon. It is also worth noting that our construction idea is simple, automatic, inexpensive and suitable for Social Media analysis.
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