Enriched Latent Dirichlet Allocation for Sentiment Analysis. Issue 4 (28th January 2020)

• Record Type: Journal Article
• Authors: Osmani, Amjad; Mohasefi, Jamshid Bagherzadeh; Gharehchopogh, Farhad Soleimanian
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-28
• Subjects: latent Dirichlet allocation (LDA) -- machine learning -- opinion mining -- sentiment analysis -- topic modelling; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12527
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: One of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other parameters. In this study, three novel topic models called date sentiment LDA (DSLDA), author–date sentiment LDA (ADSLDA), and pack–author–date sentiment LDA (PADSLDA) are proposed. The proposed models extend LDA through some extra parameters such as date, author, helpfulness, sentiment, and subtopic. The proposed models use helpfulness in the Gibbs sampling algorithm. Helpfulness is a part of readers who found the review helpful. The proposed models divide the words into two categories: the words more affected by the distribution of subtopic and the words more affected by the main topic. In this study, a new concept called pack is introduced, and a new model called PADSLDA is proposed for sentiment analysis at pack level. The proposed models outperformed the baseline models because according to evaluations results, the extra parameters can appropriately affect the generating process of words in a review. Sentiment analysis at the document level, perplexity, and topic coherence are the main parameters used in the evaluations.