A Review on Machine Learning and Deep Learning Models for Sentiment Analysis of Social Media Text Data

In conclusion, Sentiment analysis has become a crucial tool for commercial purposes, offering valuable insights into user activities and choices. By employing sentiment analyzers, various methods and algorithms within Natural Language Processing (NLP) are utilized for a comprehensive understanding. This research conducts an extensive review of diverse datasets and research works employing different machine learning techniques for sentiment analysis. Sentiment analysis using machine learning has proven to be a powerful and versatile tool for extracting valuable insights from textual data. The reviewed studies showcased a diverse range of techniques, from traditional methods like Random Forest and Word2Vec to advanced approaches such as Recurrent Neural Networks (RNN). These techniques have demonstrated high accuracy in discerning sentiment across various domains. The future holds promise for continued advancements, particularly in the realm of video sentiment analysis, where the extraction of features from frames and the application of machine learning algorithms are poised to play a pivotal role.  Overall, the research underscores the significance of machine learning in unraveling sentiment patterns, contributing to a deeper understanding of user sentiments in diverse applications and domains.

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