An improved context-aware analysis for sentimental Grass Hopper Optimization algorithm and its post affects on Twitter.

Social media platforms, namely Instagram, Facebook, Twitter, YouTube, etc. have gained a lot of attention as users used to share their views, and post videos, audio, and pictures for social networking. In near future, understanding the meaning and analyzing this enormously rising volume and size of online data will become a necessity in order to extract valuable information from them. In a similar context, the paper proposes an analysis model in two phases namely the training and the sentiment classification using the reward-based grasshopper optimization algorithm. The training architecture and context analysis of the tweet are presented for the sentiment analysis along with the ground truth processing of emotions. The proposed algorithm is divided into two phases namely the exploitation and the exploration part and creates a reward mechanism that utilizes both phases. The proposed algorithm also uses cosine similarity, dice coefficient, and euclidean distance as the input set and further processes using the grasshopper algorithm. Finally, it presents a combination of swarm intelligence and machine learning for attribute selection in which the reward mechanism is further validated using machine learning techniques. The comparative performance in terms of precision, recall, and F-measure has been measured for the proposed model in comparison to existing swarm-based sentiment analysis works. Overall, simulation analysis showed that the proposed work based on grasshopper optimization outperformed the existing approaches for Sentiment 140 by 5.93% to 10.05%, SemEval 2013 by 6.15% to 12.61%, and COVID-19 tweets by 2.72% to 9.13%. Thus, demonstrating the efficiency of the context-aware sentiment analysis using the grasshopper optimization approach.