If you are happy and you know it... tweet

Amir Asiaee T., Mariano Tepper, Arindam Banerjee, Guillermo Sapiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

Extracting sentiment from Twitter data is one of the fundamental problems in social media analytics. Twitter's length constraint renders determining the positive/negative sentiment of a tweet difficult, even for a human judge. In this work we present a general framework for per-tweet (in contrast with batches of tweets) sentiment analysis which consists of: (1) extracting tweets about a desired target subject, (2) separating tweets with sentiment, and (3) setting apart positive from negative tweets. For each step, we study the performance of a number of classical and new machine learning algorithms. We also show that the intrinsic sparsity of tweets allows performing classification in a low dimensional space, via random projections, without losing accuracy. In addition, we present weighted variants of all employed algorithms, exploiting the available labeling uncertainty, which further improve classification accuracy. Finally, we show that spatially aggregating our per-tweet classification results produces a very satisfactory outcome, making our approach a good candidate for batch tweet sentiment analysis.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages1602-1606
Number of pages5
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

Keywords

  • bayes classification
  • compressed learning
  • sparse modeling
  • supervised learning
  • svm
  • twitter sentiment analysis

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