Optimization driven actor-critic neural network for sentiment analysis in social media

Abstract

Purpose Sentiment analysis is the subfield of data mining, which is profusely used for studying the opinions of the users by analyzing their suggestions on the Web platform. It plays an important role in the daily decision-making process, and every decision has a great impact on daily life. Various techniques including machine learning algorithms have been proposed for sentiment analysis, but still, they are inefficient for extracting the sentiment features from the given text. Although the improvement in sentiment analysis approaches, there are several problems, which make the analysis inefficient and inaccurate. This paper aims to develop the sentiment analysis scheme on movie reviews by proposing a novel classifier. Design/methodology/approach For the analysis, the movie reviews are collected and subjected to pre-processing. From the pre-processed review, a total of nine sentiment related features are extracted and provided to the proposed exponential-salp swarm algorithm based actor-critic neural network (ESSA-ACNN) classifier for the sentiment classification. The ESSA algorithm is developed by integrating the exponentially weighted moving average (EWMA) and SSA for selecting the optimal weight of ACNN. Finally, the proposed classifier classifies the reviews into positive or negative class. Findings The performance of the ESSA-ACNN classifier is analyzed by considering the reviews present in the movie review database. From, the simulation results, it is evident that the proposed ESSA-ACNN classifier has improved performance than the existing works by having the performance of 0.7417, 0.8807 and 0.8119, for sensitivity, specificity and accuracy, respectively. Originality/value The proposed classifier can be applicable for real-world problems, such as business, political activities and so on.