Using Combined Lexical Resources to Identify Hashtag Types

This paper seeks to identify sentiment and non-sentiment bearing hashtags by com- bining existing lexical resources. By using a lexicon-based approach, we achieve 86.3% and 94.5% precision in identifying sentiment and non-sentiment hashtags, respectively. Moreover, results obtained from both of our classification models demonstrate that using combined lexical, emotion and word resources is more effective than using a single resource in identifying the two types of hashtags.

[1]  Xiaolong Wang,et al.  Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach , 2011, CIKM '11.

[2]  Tejashri Inadarchand Jain,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2010 .

[3]  Vasudeva Varma,et al.  Mining Sentiments from Tweets , 2012, WASSA@ACL.

[4]  Robert J. Hilderman,et al.  Evaluating the Effectiveness of Hashtags as Predictors of the Sentiment of Tweets , 2015, Discovery Science.

[5]  Philip J. Stone,et al.  Extracting Information. (Book Reviews: The General Inquirer. A Computer Approach to Content Analysis) , 1967 .

[6]  Saif Mohammad,et al.  #Emotional Tweets , 2012, *SEMEVAL.

[7]  Ari Rappoport,et al.  Enhanced Sentiment Learning Using Twitter Hashtags and Smileys , 2010, COLING.

[8]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

[9]  Ellen Riloff,et al.  Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases , 2014, EMNLP.

[10]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

[11]  Ellen Riloff,et al.  Bootstrapped Learning of Emotion Hashtags #hashtags4you , 2013, WASSA@NAACL-HLT.

[12]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[13]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[14]  A. Kilgarriff.  *SEM 2012: The First Joint Conference on Lexical and Computational Semantics , 2012 .

[15]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[16]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL 2006.

[17]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[18]  Pablo Gervás,et al.  SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis , 2012, LREC.