SAIL: Sentiment Analysis Using Semantic Similarity and Contrast Features

Abstract

This paper describes our submission to SemEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexicon based one, augmented by some preprocessing, including detection of Multi-Word Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet level. Feature extraction is repeated for sub-strings and contrasting sub-string features are used to better capture complex phenomena like sarcasm. The resulting supervised system, using a Naive Bayes model, achieved high performance in classifying entire tweets, ranking 7th on the main set and 2nd when applied to sarcastic tweets.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
AD1171470

Entities

People

  • Alexandros Potamianos
  • Colin Vaz
  • Jesse Bisogni
  • Michael Falcone
  • Nikolaos Malandrakis
  • Shrikanth Narayanan

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Linguistics
  • Computer Science
  • Contrast
  • Data Mining
  • Data Sets
  • Extraction
  • Feature Extraction
  • Feature Selection
  • Frequency
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Models
  • Online Communications
  • Semantic Models
  • Social Media
  • Statistics

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Information Retrieval
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval