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  1. Proceedings of the 7th Forum for Information Retrieval Evaluation (FIRE '15)
  2. HBE: Hashtag-Based Emotion Lexicons for Twitter Sentiment Analysis
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Context-driven Dimensionality Reduction for Clustering Text Documents
An Empirical Comparison of Statistical Term Association Graphs with DBpedia and ConceptNet for Query Expansion
Document Retrieval Metrics for Program Understanding
HBE: Hashtag-Based Emotion Lexicons for Twitter Sentiment Analysis
Automatic Identification of Conceptual Structures using Deep Boltzmann Machines
Construction of a Semi-Automated model for FAQ Retrieval via Short Message Service
OnForumS: The Shared Task on Online Forum Summarisation at MultiLing'15
word2vec or JoBimText?: A Comparison for Lexical Expansion of Hindi Words
A Comparative Study on Different Translation Approaches for Query Formation in the Source Retrieval Task
MESS: A Multilingual Error based String Similarity measure for transliterated name variants

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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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