A computational approach to finding contradictions in user opinionated text

Chuqin Li, Xi Niu, Ahmad Al-Doulat, Noseong Park

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

Abstract

The rapid growth of Web 2.0 and wide popularity of social media have brought the challenge of digesting and understanding large amounts of user-generated text. Automatically finding contradictions from user opinionated text is a potential solution to help sense-making and decision-making process from those user opinions. However, the problem of contradiction detection is understudied in social media analysis field. This study presents a computational approach to detecting contradictions in user opinionated text. Specifically, a typology of contradictions was proposed, and then the state-of-art deep learning models were adopted and enhanced by three methods of incorporating sentiment analysis. The enhanced models were evaluated with Amazon's customer reviews. The best model was selected and applied to a collection of tweets from Twitter to demonstrate its usefulness in understanding contradiction semantically and quantitatively in a large amount of user opinionated text.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
EditorsAndrea Tagarelli, Chandan Reddy, Ulrik Brandes
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages351-356
Number of pages6
ISBN (Electronic)9781538660515
DOIs
Publication statusPublished - 2018 Oct 24
Event10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain
Duration: 2018 Aug 282018 Aug 31

Publication series

NameProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Conference

Conference10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
CountrySpain
CityBarcelona
Period18/8/2818/8/31

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Communication
  • Computer Networks and Communications
  • Information Systems and Management

Fingerprint Dive into the research topics of 'A computational approach to finding contradictions in user opinionated text'. Together they form a unique fingerprint.

Cite this