A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA

Ozyurt B., Akcayol M. A.

Expert Systems with Applications, vol.168, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 168
  • Publication Date: 2021
  • Doi Number: 10.1016/j.eswa.2020.114231
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Sentiment analysis, Opinion mining, Aspect based sentiment analysis, Aspect extraction
  • Gazi University Affiliated: Yes


© 2020 Elsevier LtdWith the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.