Semantic Patterns for Sentiment Analysis of Twitter

  • Hassan Saif
  • Yulan He
  • Miriam Fernandez
  • Harith Alani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8797)

Abstract

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Keywords

Sentiment Analysis Semantic Patterns Twitter 

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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 UniversityUnited Kingdom
  2. 2.School of Engineering and Applied ScienceAston UniversityUnited Kingdom

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