Aspect based multimodal sentiment analysis of product reviews using deep learning techniques

Abstract

Sentiment analysis plays a crucial role in understanding customer opinions, particularly in product reviews. Traditional approaches primarily focus on textual data; however, with the rise of social media, incorporating multimodal data, including text and emojis, enhances sentiment analysis accuracy. This research introduces a multimodal aspect-based sentiment analysis (MABSA) framework, integrating textual and emoji representations for Samsung M21 product reviews from Flipkart. The methodology involves data preprocessing, aspect extraction, sentiment grouping, and feature extraction using deep learning (DL) techniques. Bidirectional long shortterm memory (Bi-LSTM) networks are employed for classification, leveraging Word2Vec, Emoji2Vec, and bidirectional encoder representations from transformers (BERT) embeddings. Experimental results show that BERT with Bi-LSTM outperforms Word2Vec with Bi-LSTM, achieving 95.6% accuracy in aspect prediction and 96.28% accuracy in sentiment classification. Comparative analysis with existing models highlights the superiority of the MASAT model, effectively integrating implicit aspects, emoticons, and emojis. The study demonstrates the importance of multimodal sentiment analysis for a more comprehensive understanding of user opinions, offering valuable insights for businesses to enhance customer satisfaction.