Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine

TL;DR

This paper introduces a novel supervised joint sentiment-topic modeling approach using a modified Restricted Boltzmann Machine with an added sentiment layer, demonstrating improved performance in sentiment classification and information retrieval.

Contribution

It proposes a new neural network-based structure for joint sentiment and topic modeling, integrating sentiment analysis into a generative RBM framework.

Findings

Effective in sentiment classification

Improves information retrieval accuracy

Demonstrates efficiency over existing models

Abstract

Recently by the development of the Internet and the Web, different types of social media such as web blogs become an immense source of text data. Through the processing of these data, it is possible to discover practical information about different topics, individuals opinions and a thorough understanding of the society. Therefore, applying models which can automatically extract the subjective information from the documents would be efficient and helpful. Topic modeling methods, also sentiment analysis are the most raised topics in the natural language processing and text mining fields. In this paper a new structure for joint sentiment-topic modeling based on Restricted Boltzmann Machine (RBM) which is a type of neural networks is proposed. By modifying the structure of RBM as well as appending a layer which is analogous to sentiment of text data to it, we propose a generative structure for joint sentiment topic modeling based on neutral networks. The proposed method is supervised and trained by the Contrastive Divergence algorithm. The new attached layer in the proposed model is a layer with the multinomial probability distribution which can be used in text data sentiment classification or any other supervised application. The proposed model is compared with existing models in the experiments such as evaluating as a generative model, sentiment classification, information retrieval and the corresponding results demonstrate the efficiency of the method.