Adapting Sentiment Lexicons Using Contextual Semantics for Sentiment Analysis of Twitter

  • Hassan Saif
  • Yulan He
  • Miriam Fernandez
  • Harith Alani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8798)

Abstract

Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words’ sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.

Keywords

Sentiment analysis Semantics Lexicon adaptation Twitter 

Notes

Acknowledgment

This work was supported by the EU-FP7 project SENSE4US (grant no. 611242).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hassan Saif
    • 1
  • Yulan He
    • 2
  • Miriam Fernandez
    • 1
  • Harith Alani
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK
  2. 2.School of Engineering and Applied ScienceAston UniversityBirminghamUK

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