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In this paper, a novel sentiment analysis model is implemented, which consists of six stages: (i) Pre-processing, (ii) Keyword extraction and its sentiment categorisation, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Initially, the Mongodb documented tweets are subjected to pre-processing that includes steps such as stop word removal, stemming, and blank space removal. Accordingly, from the pre-processed tweets, the keywords are extracted. Based on the extracted keywords, the prevailing semantic words are extracted after classifying the sentimental keywords. Further, the evaluation of the semantic similarity score with the keywords takes place. Also, it exploits joint holoentropy and cross holoentropy. Here, the extraction of weighted holoentropy features is the main contribution, where a weight function is multiplied by the holoentropy features. To improve the performance of classification, a constant term is used for calculating weight function. It is tuned or optimised in such a way that the accuracy of the proposed method is better. The optimisation strategy uses the hybrid model that merges Particle Swarm Optimisation (PSO) into Whale Optimisation Algorithm (WOA). Hence, the proposed algorithm is named as Swarm Velocity-based WOA (SV-WOA). Finally, the analysis is done to prove the efficiency of the proposed model.

Weighted holoentropy-based features with optimised deep belief network for automatic sentiment analysis: reviewing product tweets