Sentiment Analysis and Machine Learning Algorithm Implementation on Flipkart Product Customer Reviews

Abstract

Sentiment analysis is a vital aspect of data mining that aims to prize private information from textual data. With the adding fashionability of online shopping platforms like Flipkart, assaying client feedback through product reviews have become a pivotal task for businesses to ameliorate their products and services. In recent times, researchers have explored the operation of machine knowledge algorithms for sentiment analysis of Flipkart product reviews. The results of this approach have demonstrated great promisein directly classifying the opposition of reviews as positive, negative, or neutral. colorful ways similar as Naive Bayes, SVM, Random Forest, Decision Trees, deep literacy, and rule-grounded approaches have been used for sentiment analysis on Flipkart reviews. also, preprocessing ways like Bag of Words, TF-IDF, Word Embeddings, and N-grams have been employed for point birth. This literature check highlights ten exploration papers published from 2017 to 2022, which bandy the perpetration of sentiment analysis and machine literacy algorithms on Flipkart product reviews. The findings of these studies give perceptivity into the effectiveness of different approaches and ways for sentiment analysis on Flipkart product reviews, and can guide unborn exploration in this field. We proposed a model called "GRUUSE" (Gated Recurrent Unit - Universal Sentence Encoder), which can directly classify the product reviews into any one of the three orders positive, negative or neutral. It is a neural network model that converts variable-length words into numerical representations of a fixed length using GRU cells. Tasks like phrase similarity or grouping are made possible by these representations, which capture semantic meaning.