Comprehensive Analysis of Twitter Sentiment Using Machine Learning Algorithms for Enhanced Sentiment Prediction

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L. Sudha Rani, S. Zahoor-Ul Huq, C. Shoba Bindu

Abstract

The principal aim of e-commerce systems is to improve the consumer experience, with customer input being essential to this objective. This study introduces an innovative method for analysing tweet data to enhance customer happiness. Key tweet features were retrieved to capture the sentiment expressed in customer tweets by utilising a combination of n-gram models and word embeddings. The features were subsequently employed to construct classification models utilising Support Vector Machines (SVM) and Artificial Neural Networks (ANN), both of which are frequently applied in sentiment analysis. A model based on Convolutional Neural Networks (CNN) was proposed to enhance the accuracy of classifying sentiment by categorizing tweets as either positive or negative. The study's results indicated that the CNN model surpassed both the SVM and ANN models, exhibiting more accuracy in sentiment classification of tweets. In addition to its precision, the CNN model offered significant insights into the interrelations among different tweet categories, enhancing comprehension of client mood and views. This extensive study underscores the need of understanding the complexities of customer feedback to improve e-commerce strategies and consumer delight as well as the efficiency of the CNN model for sentiment categorisation. By means of exact classification and analysis of sentiment in real-time twitter data, e-commerce systems may more successfully evaluate customer impressions and react dynamically to enhance their whole experience.

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