Sentiment analysis from Customer-generated online videos on product review using topic modeling and Multi-attention BLSTM

With the popularity of social websites and mobile applications including Instagram, YouTube, TikTok, etc., online videos shared by customers presenting their thoughts and reviews on products are posted daily in increasing numbers. Such online videos containing Voice of Customer (VOC) are precious for product designers or managers to capture customer sentiment and understand customer preference. For this purpose, we propose a novel method for analyzing customer sentiment from online videos on product review. Firstly, latent Dirichlet allocation (LDA) modeling is applied to identify the topics from the online videos after data preprocessing. Then sentiment polarity corresponding to each topic of each speaker in videos can be identified using our newly designed multi-attention bi-directional LSTM (BLSTM(MA)), which can better mine complex relationships among a speaker's sentiments on different topics. This paper is of great practical value for company managers and researchers to better understand a large number of customer opinions on specific products. To explain the application of this method and prove its effectiveness, two cases respectively on smartphones and several pub-lished datasets are developed finally.

Automated summary

With the increasing popularity of social websites and mobile applications, online videos shared by customers providing their thoughts and reviews on products have become more prevalent. This paper proposes a novel method for analyzing customer sentiment from these online videos. The method involves applying latent Dirichlet allocation (LDA) modeling for topic identification and using a newly designed multi-attention bi-directional LSTM (BLSTM(MA)) to determine sentiment polarity. The research is of great practical value for company managers and researchers to better understand customer opinions on specific products.