Article: Classification of the sentiment using African vultures spider monkey optimisation based SqueezeNet technique Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.16 No.2 pp.164 - 190 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 AVSMO_SqueezeNet 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Konda Adilakshmi; Malladi Srinivas; Anuradha Kodali; Srilakshmi Vellanki

Addresses: Department of CSE, KoneruLakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India; Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Bachupally, Hyderabad – 500090, India ' Department of CSE, KoneruLakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India ' Department of CSE, GokarajuRangaraju Institute of Engineering and Technology (GRIET), Bachupally, Hyderabad – 500090, India ' Department of CSE, GokarajuRangaraju Institute of Engineering and Technology (GRIET), Bachupally, Hyderabad – 500090, India

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 AVSMO_SqueezeNet 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.

Keywords: SqueezeNet; aquila optimiser; AO; African vultures optimisation algorithm; AVOA; SailFish optimiser; SFO; and spider monkey optimisation; SMO.

DOI: 10.1504/IJCVR.2026.151536

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.2, pp.164 - 190

Received: 30 Nov 2022
Accepted: 15 Nov 2023
Published online: 05 Feb 2026 *

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

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