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Sentiment Analysis on Twitter through Topic-Based Lexicon Expansion

  • Zhixin Zhou
  • Xiuzhen Zhang
  • Mark Sanderson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8506)

Abstract

Supervised learning approaches are domain-dependent and it is costly to obtain labeled training data from different domains. Lexicon-based approaches enjoy stable performance across domains, but often cannot capture domain-dependent features. It is also hard for lexicon-based classifiers to identify the polarities of abbreviations and misspellings, which are common in short informal social text but usually not found in general sentiment lexicons. We propose to overcome this limitation by expanding a general lexicon with domain-dependent opinion words as well as abbreviations and informal opinion expressions. The expanded terms are automatically selected based on their mutual information with emoticons. As there is an abundant amount of emoticon-bearing tweets on Twitter, our approach provides a way to do domain-dependent sentiment analysis without the cost of data annotation. We show that our technique leads to statistically significant improvements in classification accuracies across 56 topics with a state-of-the-art lexicon-based classifier. We also present the expanded terms, and show the most representative opinion expressions obtained from co-occurrence with emoticons.

Keywords

Sentiment Analysis Opinion Word Sentiment Lexicon Label Training Data Semantic Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhixin Zhou
    • 1
  • Xiuzhen Zhang
    • 1
  • Mark Sanderson
    • 1
  1. 1.Department of Computer Science and ITRMIT UniversityMelbourneAustralia

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