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The Twitter messaging service has turned out to be a domain for news consumers and patrons to convey their sentiments. Capturing these emotions or sentiments in an accurate manner remains a major challenge for analysts. Moreover, the Twitter data include both spam and authentic contents that often affects accurate sentiment categorization. This paper introduces a new customized BERT (Bidirectional Encoder Representations from Transformers) based sentiment classification. The proposed work consists on pre-processing and tokenization step followed by a customized BERT based classification via optimization concept. Initially, the collected raw tweets are pre-processed via "stop word removal, stemming and blank space removal". Prevailing semantic words are acquired, from which the tokens (meaningful words) are extracted in the tokenization phase. Subsequently, these extracted tokens will be subjected to classification via optimized BERT, which weights and biases are optimally tuned by Standard Lion Algorithm (LA). In addition, the maximum sequence length of BERT encoder is updated with standard LA. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to different performance measures.

Customized BERT with Convolution Model : A New Heuristic Enabled Encoder for Twitter Sentiment Analysis