Identifying the Overlap between Election Result and Candidates' Ranking Based on Hashtag-Enhanced, Lexicon-Based Sentiment Analysis

Rezvaneh Rezapour, Lufan Wang, Omid Abdar, Jana Diesner

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

Abstract

The popularity and availability of Twitter as a service and a data source have fueled the interest in sentiment analysis. Previous research has shed light on the challenges that contextualizing effects and linguistic complexities pose for the accurate sentiment classification of tweets. We test the effect of adding manually-annotated, corpus-based hashtags to a sentiment lexicon, finding that this step in combination with negation detection increases prediction accuracy by about 7%. We then use our enhanced model to identify and rank the candidates of the Republican and Democratic Party of the 2016 New York primary election by the decreasing ratio of tweets that mentioned these individuals and had positive valence, and compare our results to the election outcome.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-96
Number of pages4
ISBN (Electronic)9781509048960
DOIs
StatePublished - Mar 29 2017
Event11th IEEE International Conference on Semantic Computing, ICSC 2017 - San Diego, United States
Duration: Jan 30 2017Feb 1 2017

Publication series

NameProceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017

Other

Other11th IEEE International Conference on Semantic Computing, ICSC 2017
Country/TerritoryUnited States
CitySan Diego
Period1/30/172/1/17

Keywords

  • Lexicon Based Approach
  • Natural Language Processing
  • Opinion Mining
  • Sentiment Analysis
  • Twitter

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications

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