GRIET CSE 2023 24 BatchA11 Major Proj African Vulture and Spider Monkey Opt Amazon Product Reviews

Understanding user emotions through their comments on material in social media platforms or
product reviews is crucial, and sentiment classification is a fundamental task in sentiment
analysis. Uncertain words, rejection words, and other words provide a number of difficult
problems during sentiment categorization. Thus, an effective method is suggested, namely.
Utilizing the SqueezeNet approach, African Vultures Spider Monkey Optimization (AVSMO)
classes sentiment. An Amazon review paper is first taken into account as input, and then it moves
on to the tokenization stage, where Bidirectional Encoder Representations from Transformers
(BERT) are employed. Sentences are divided into smaller units, known as tokens, during the
tokenization process. Then, correct features are extracted from the tokenized output using feature
extraction. correct features include the number of words in a review, the number of sentences in
a review, emoticons, exclamation points, punctuation marks, and elongated words. word jumble.
Along with term frequency, other extracted data includes question marks and inverse document
frequency (TF-IDF). The AVSMO algorithm that has been tweaked for sentiment classification
is finally applied using SqueezeNet.