Identifying Biased Users in Online Social Networks to Enhance the Accuracy of Sentiment Analysis: A User Behavior-Based Approach

TL;DR

This paper introduces a neural network approach to identify biased users in online social networks based on psychological behaviors, improving sentiment analysis accuracy by filtering out overly positive or negative users.

Contribution

It presents a novel user behavior-based neural network classifier to detect biased users, enhancing sentiment analysis by accounting for user bias rather than just review content.

Findings

Biased users can be predicted with up to 89% accuracy.

The method effectively categorizes overly positive and negative users.

Improves sentiment analysis accuracy by identifying user bias.

Abstract

The development of an automatic way to extract user opinions about products, movies, and foods from online social network (OSN) interactions is among the main interests of sentiment analysis and opinion mining studies. Existing approaches in the sentiment analysis domain mostly do not discriminate the sentences of different types of users, even though some users are always negative and some are always positive. Thus, finding a way to identify these two types of user is significant because their attitudes can change the analysis of user reviews of businesses and products. Due to the complexity of natural language processing, pure text mining methods may lead to misunderstandings about the exact nature of the sentiments expressed in review text. In this study, we propose a neural network classifier to predict the presence of biased users on the basis of users' psychological behaviors. The identification of the psychological behaviors of users allows us to find overly positive and overly negative users and to categorize these users' attitudes regardless of the content of their review texts. The experiment result indicates that the biased users can be predicted based on user behavior at an accuracy rate of 89%, 67% and 81% for three different datasets.