FBO‐RNN: Fuzzy butterfly optimization‐based RNN‐LSTM for extracting sentiments from Twitter Emoji database

Abstract

SummarySocial media networks have seen a significant increase in purpose and scale. Sentimental analysis is a component of social networking platforms that uses shared material to infer information about individual sentiments and emotions. In recent years, sentiment analysis (SA) research has grown in popularity. Twitter is the most popular social media platform, with users from many languages and cultures participating. Emojis are used by users to express themselves, and social media platforms contain a wide range of symbols, emotions, and opinions. A novel framework for SA based on Emojis is presented in this article. Initially, the noise‐free videos and images are filtered. The dictionary of Jieba was obtained by adding the English Emoji lexicon and English Internet slang lexicon to segment English text. Initially, the Emojis are converted into textual features. Different sentiment classes such as positive, very positive, neutral, negative, and very negative classes are classified using long short‐term memory (LSTM) in the recurrent neural network (RNN)‐based Fuzzy Butterfly Optimization (FBO) algorithm. The freeware WEKA software tool with different evaluation measures performs experimental investigations. Ultimately, the proposed model demonstrates superior results in the case of SA than other state‐of‐the‐art methods.