Joint sentiment/topic model for sentiment analysis

TLDR

Sentiment analysis aims to automatically detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a joint sentiment/topic model (JST) that simultaneously detects sentiment and topic from text using a probabilistic LDA‑based framework. The JST model is fully unsupervised, built on LDA, and was evaluated on a movie review dataset to classify sentiment polarity, with minimal prior information explored to improve accuracy. Preliminary experiments demonstrate promising results for JST.

Abstract

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.