Sentiment strength prediction using auxiliary features

Huijun CHEN, Haoran XIE, Xin LI, Fu Lee WANG, Yanghui RAO*, Tak Lam WONG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)peer-review

3 Citations (Scopus)

Abstract

With an increasingly large amount of sentimental information embedded in online documents, sentiment analysis is quite valuable to product recommendation, opinion summarization, and so forth. Different from most works on identifying documents' qualitative affective information, this research focuses on the measurement of users' intensity over each sentimental category. Affect indicates positive or negative sentiment, while cognition includes certainty and tentative. Thus, our research can help bridge the cognitive and affective gaps between users and documents. The contributions of this study are twofold: (i) we proposed a neural network-based framework to sentiment strength prediction by convolving hybrid vectors, and (ii) we considered words jointly with a set of linguistic features for enhancing model robustness and adaptiveness. By exploiting the auxiliary features of sentiments from the corpus, the proposed model did not rely on well-established lexicons, and showed its robustness over sparse words. Experiments on six corpora validated the effectiveness of our sentiment strength prediction method.

Original languageEnglish
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages5-14
Number of pages10
ISBN (Electronic)9781450349147
ISBN (Print)9781450349147
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Conference

Conference26th International World Wide Web Conference, WWW 2017 Companion
CountryAustralia
CityPerth
Period3/04/177/04/17

Keywords

  • Convolutional neural network
  • Hybrid features
  • Sentiment strength

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