Semantic properties of customer sentiment in tweets

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

4 Scopus citations

Abstract

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers' opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE 28th International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014
PublisherIEEE Computer Society
Pages657-663
Number of pages7
ISBN (Print)9781479926527
DOIs
StatePublished - Jan 1 2014
Event28th IEEE International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014 - Victoria, BC, Canada
Duration: May 13 2014May 16 2014

Publication series

NameProceedings - 2014 IEEE 28th International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014

Other

Other28th IEEE International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014
CountryCanada
CityVictoria, BC
Period5/13/145/16/14

Keywords

  • text analytics; tweet analysis; document similarity; clustering; topic modeling; part-of-speech tagging

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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    Ko, E. E., & Klabjan, D. (2014). Semantic properties of customer sentiment in tweets. In Proceedings - 2014 IEEE 28th International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014 (pp. 657-663). [6844713] (Proceedings - 2014 IEEE 28th International Conference on Advanced Information Networking and Applications Workshops, IEEE WAINA 2014). IEEE Computer Society. https://doi.org/10.1109/WAINA.2014.151