Leveraging the crowd to improve feature-sentiment analysis of user reviews

Shih Wen Huang, Pei Fen Tu, Wai-Tat Fu, Mohammad Amanzadeh

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

Abstract

Crowdsourcing and machine learning are both useful techniques for solving difficult problems (e.g., computer vision and natural language processing). In this paper, we propose a novel method that harnesses and combines the strength of these two techniques to better analyze the features and the sentiments toward them in user reviews. To strike a good balance between reducing information overload and providing the original context expressed by review writers, the proposed system (1) allows users to interactively rank the entities based on feature-rating, (2) automatically highlights sentences that are related to relevant features, and (3) utilizes implicit crowdsourcing by encouraging users to provide correct labels of their own reviews to improve the feature-sentiment classifier. The proposed system not only helps users to save time and effort to digest the often massive amount of user reviews, but also provides real-time suggestions on relevant features and ratings as users generate their own reviews. Results from a simulation experiment show that leveraging on the crowd can significantly improve the feature-sentiment analysis of user reviews. Furthermore, results from a user study show that the proposed interface was preferred by more participants than interfaces that use traditional noun-adjective pair summarization, as the current interface allows users to view feature-related information in the original context.

Original languageEnglish (US)
Title of host publicationIUI 2013 - Proceedings of the 18th International Conference on Intelligent User Interfaces
Pages3-13
Number of pages11
DOIs
StatePublished - Mar 19 2013
Event18th International Conference on Intelligent User Interfaces, IUI 2013 - Santa Monica, CA, United States
Duration: Mar 19 2013Mar 22 2013

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI

Other

Other18th International Conference on Intelligent User Interfaces, IUI 2013
CountryUnited States
CitySanta Monica, CA
Period3/19/133/22/13

Keywords

  • Crowdsourcing
  • Human computation
  • Interactive machine learning
  • Sentiment analysis
  • User generated content

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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  • Cite this

    Huang, S. W., Tu, P. F., Fu, W-T., & Amanzadeh, M. (2013). Leveraging the crowd to improve feature-sentiment analysis of user reviews. In IUI 2013 - Proceedings of the 18th International Conference on Intelligent User Interfaces (pp. 3-13). (International Conference on Intelligent User Interfaces, Proceedings IUI). https://doi.org/10.1145/2449396.2449400