Sentiment Analysis of Twitter Big Data Using Hybrid HNN-BiGRU and Semantic Lexicons with Puffer Fish Optimized Glove

Abstract

Twitter has millions of active users and is a significant microblogging platform. These users use Twitter to give their thoughts on various events using hashtags also to make status updates known as tweets. As a result, Twitter is regarded as a significant real-time streaming source as well as a reliable and accurate opinion indicator. Due to Twitter’s massive data generation volume, it is challenging to manually scan the entire collection. Given the massive volume of data supplied by Twitter, it is challenging to manually scan the entire collection. So, a hybrid deep learning algorithm is developed to analyse the sentiment of the user. This research incorporates a variety of techniques like pre-processed using tokenization, stop word removal, stemming, Removal of hyperlinks and numbers, Abbreviation extending and spell correction. After that, use Semantic Lexicons with Puffer Fish Optimized GLOVE (SLPFOG) to extract features and convert words into vectors. The, reduce the dimension of the extracted features by applying the Laplacian Eigen map. To forecast the user sentiment of Twitter Big data, a hybrid Hopfield Neural Network—Bidirectional Gated Recurrent Unit (HNN-BiGRU) technique was created. The proposed hybrid HNN-BiGRU approach has an accuracy of 96%, specificity of 99%, NPV of 99% and MCC of 97%. Thus, the hybrid deep learning algorithm is the best option for sentimental analysis of twitter big data because they achieves relatively high accuracy with respect to basic algorithms without sacrificing the interpretability of the learning results.