Sentiment Analysis Using Improved Atom Search Optimizer With a Simulated Annealing and ReLU Based Gated Recurrent Unit

Abstract

Social media has become an indispensable part of our daily lives in recent times. On social media, users commonly express their thoughts and opinions by sharing a substantial number of reviews and feedback. Twitter is one of the social media platforms with the best growth, and it also serves as a news and business tool. However, large features slow down and lessen the sensitivity of analysis sentiment. It remains difficult to select and classify features in the best possible way. Since feature selection plays a critical role in sentiment analysis. This study proposed the ReLU based Gated Recurrent Unit (ReLU-GRU) for Twitter sentiment analysis to classify the emotions. Covid-19, Sentiment-140 and twitter emoji datasets are exploited to perform the research. Initially, a pre-processing is done through tokenization, stemming, adding part of speech, and punctuation removal. After that, Bag of Words (BoW), Latent Dirichlet Analysis (LDA), Term Frequency, and Inverse Document Frequency (TF-IDF) is applied for extracting the features. Followed by that, classification is carried out by using the proposed Improved Atom Search Optimizer (ASO) and a Simulated Annealing (SA) method. Finally, in the classification stage, ReLU-GRU is proposed for classifying the chosen features into various classes. From the outcomes, it evidently shows that proposed ReLU-GRU has outperformed existing methods by obtaining 97.87% and 96.52% of accuracy on Covid-19 and Sentiment-140 datasets.

Keywords

• Improved atom search optimizer
• simulated annealing
• ReLU based gated recurrent unit
• sentiment analysis
• twitter datasets