TY - GEN
T1 - HBE
T2 - 7th Annual Meeting of the Forum for Information Retrieval Evaluation, FIRE 2015
AU - Koto, Fajri
AU - Adriani, Mirna
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/12/4
Y1 - 2015/12/4
N2 - In this paper we report the first effort of constructing emo- tion 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 ac- cording 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 Emo- tion (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.
AB - In this paper we report the first effort of constructing emo- tion 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 ac- cording 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 Emo- tion (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.
KW - Emotion lexicon
KW - Hashtag
KW - Polarity
KW - Sentiment analysis
KW - Sub-jectivity
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84959281988&partnerID=8YFLogxK
U2 - 10.1145/2838706.2838718
DO - 10.1145/2838706.2838718
M3 - Conference contribution
AN - SCOPUS:84959281988
T3 - ACM International Conference Proceeding Series
SP - 31
EP - 34
BT - FIRE 2015 - Proceedings of the 7th Annual Meeting of the Forum for Information Retrieval Evaluation
A2 - Majumder, Prasenjit
A2 - Mitra, Mandar
A2 - Agrawal, Madhulika
A2 - Mehta, Parth
PB - Association for Computing Machinery
Y2 - 4 December 2015 through 6 December 2015
ER -