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This work develops a novel customized BERT-oriented sentiment classification that encompasses two main phases: pre-processing and tokenization, and a \u201cCustomized Bidirectional Encoder Representations from Transformers (BERT)\u201d-based classification. At first, the gathered raw tweets are pre-processed under stop-word removal, stemming and blank space removal. After pre-processing, the semantic words are obtained, from which the meaningful words (tokens) are extracted in the tokenization phase. Consequently, these extracted tokens are classified via optimized BERT, where biases and weight are tuned optimally by Particle-Assisted Circle Updating Position (PA-CUP). Moreover, the maximal sequence length of the BERT encoder is updated using standard PA-CUP. Finally, the performance analysis is carried out to substantiate the enhancement of the proposed model. <\/jats:p>","DOI":"10.1142\/s1469026821500152","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T09:08:33Z","timestamp":1633943313000},"source":"Crossref","is-referenced-by-count":8,"title":["Heuristic-Assisted BERT for Twitter Sentiment Analysis"],"prefix":"10.1142","volume":"20","author":[{"given":"Gokul","family":"Yenduri","sequence":"first","affiliation":[{"name":"CSE Department, Vlits,Vignan\u2018S University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. 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