How much does word sense disambiguation help in sentiment analysis of micropost data?

• Chiraag Sumanth ^[2] ; Diana Inkpen ^[1]
  1. [1] University of Ottawa

     University of Ottawa

     Canadá

     
  2. [2] PES Institute of Technology Bangalore, India
• Localización: 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2015: Workshop Proceedings : 17 September 2015 Lisboa, Portugal / Alexandra Balahur Dobrescu (ed. lit.), Erik van der Goot (ed. lit.), Piek Vossen (ed. lit.), Andrés Montoyo Guijarro (ed. lit.), 2015, ISBN 978-1-941643-32-7, págs. 115-121
• Idioma: inglés
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• Resumen

  • This short paper describes a sentiment analysis system for micro-post data that includes analysis of tweets from Twitter and Short Messaging Service (SMS) text messages. We discuss our system that makes use of Word Sense Disambiguation techniques in sentiment analysis at the message level, where the entire tweet or SMS text was analysed to determine its dominant sentiment. Previous work done in the area of Word Sense Disambiguation does not throw light on its influence on the analysis of social-media text and micropost data, which is what our work aims to achieve. Our experiments show that the use of Word Sense Disambiguation alone has resulted in an improved sentiment analysis system that outperforms systems built without incorporating Word Sense Disambiguation.