A Comprehensive Empirical study of Deep learning algorithms for the Efficient Social Media Data Analytics

Abstract

Sentiment analysis, the study of emotions conveyed in language, holds increasing significance for various practical applications. This research undertakes a thorough investigation comparing different sentiment analysis algorithms, including traditional methods like Naïve Bayes and Logistic Regression, alongside modern techniques like Recurrent Neural Networks and Transformer models. Using a meticulously curated dataset encompassing various text types and emotions, the research team evaluated the accuracy and efficiency of each algorithm, revealing insights into their strengths and weaknesses. While both conventional and contemporary algorithms demonstrate precision, disparities in accuracy were observed. The project's overarching goal is to develop a robust sentiment analysis system capable of automatically categorizing reviews as positive, negative, or neutral based on conveyed sentiments, leveraging a dataset comprising textual reviews across products, services, and experiences. Subsequent stages involve preprocessing the text data and feature extraction to enhance system performance.