March 3, 2026

Classification of the sentiment using African vultures spider monkey optimisation based SqueezeNet technique

Key Points

Sentiment classification achieves a precision of 0.878, illustrating the effectiveness of the avsmotechnique.
Key features for classification are extracted after tokenisation using BERT, optimizing the process.
SqueezeNet is utilized for sentiment classification, showcasing efficient performance due to the proposed technique.
This technique suggests better accuracy in understanding sentiments, highlighting its potential for practical applications.

Abstract

Sentiment classification is a precise chore in the categorisation of text, which intends to categorise the documents by their reviews. Analysation of sentiment is a process of extracting emotional content from the texts. An analysis of sentiment is a fundamental task, which is necessary for an understandable user. Therefore, an effective technique is proposed called the AVSMOSqueezeNet technique for the classification of sentiment Firstly, the Amazon review document is assumed as input and then it is given to the tokenisation phase, where BERT is used. After the phase of tokenisation, the feature extraction is completed for extracting appropriate features for the classification of sentiment. Lastly, sentiment classification is performed utilising Squeeze Net which is tuned by the proposed AVSMO approach. However, the newly AVSMO technique is devised by an amalgamation of AVOA and SMO techniques. Furthermore, the proposed technique achieved maximum precision of 0. 878, recall of 0. 887, and F-measure of 0. 883.

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