SSTSA: A Self-Supervised Topic Sentiment Analysis Using Semantic Similarity Measures and Transformers

Azam Seilsepour (), Reza Ravanmehr and Ramin Nassiri ()
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Azam Seilsepour: Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Reza Ravanmehr: Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Ramin Nassiri: Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 06, 2269-2307

Abstract: The exponentially increasing amount of data generated by the public on social media platforms is a precious source of information. It can be used to find the topics and analyze the comments. Some researchers have extended the Latent Dirichlet Allocation (LDA) method by adding a sentiment layer to simultaneously find the topics and their related sentiments. However, most of these approaches do not achieve admirable accuracy in Topic Sentiment Analysis (TSA), particularly when there is insufficient training data or the texts are complex, ambiguous, and short. In this paper, a self-supervised novel approach called SSTSA is proposed for TSA that extracts the hidden topics and analyzes the total sentiment related to each topic. The SSTSA proposes a new method called Pseudo-label Generator. For this purpose, first, it employs semantic similarity and Word Mover’s Distance (WMD) measures. Then, the document embedding technique is employed to semantically estimate the sentiment orientation of samples and generate the pseudo-labels (positive or negative). Afterward, a hybrid classifier composed of a pre-trained Robustly Optimized BERT (RoBERTa) and a Long Short-Term Memory (LSTM) model is trained to predict the sentiment of unseen data. The evaluation results on different datasets of various domains demonstrate that the SSTSA outperforms similar unsupervised/self-supervised methods.

Keywords: Topic sentiment analysis; self-supervised sentiment analysis; unsupervised sentiment analysis; RoBERTa; LSTM; transformers (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500736

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