NDLI: HBE: Hashtag-Based Emotion Lexicons for Twitter Sentiment Analysis

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HBE: Hashtag-Based Emotion Lexicons for Twitter Sentiment Analysis

Content Provider	ACM Digital Library
Author	Adriani, Mirna Koto, Fajri
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.
Starting Page	31
Ending Page	34
Page Count	4
File Format	PDF
ISBN	9781450340045
DOI	10.1145/2838706.2838718
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-12-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hashtag Sentiment analysis Subjectivity Emotion lexicon Polarity Twitter
Content Type	Text
Resource Type	Article

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