A Review on Sentiment Analysis Methodologies, Practices and Applications with Machine Learning

Sentiment analysis is the process of identifying and collecting valuable information using data-driven analytics, social media, and customer feedback. The purpose of sentiment analysis is to determine the overall sentiment in the trial, whether it was positive, negative, or neutral. Sentiment analysis of Twitter data has become important due to the large amount of user-generated content available on the platform. This work involves analyzing tweets to measure user sentiment around a particular topic, product or brand. Sentiment analysis of tweets can often contain slang, typos and other idioms due to their length, making sentiment analysis a daunting task. In addition, tweets may contain serious content, often containing cynicism and sarcasm, which may go unnoticed by the use of psychological techniques. Despite these challenges, sentiment analysis of Twitter data has many applications, from public opinion tracking about political campaigns to people watching. This has led to the development of advanced learning models, which are deep learning and neural networks that provide the accuracy and intelligence of Twitter sentiment analysis. However, more research is needed to address the problems of the current model and improve its ability to identify and interpret sentiment in Twitter data.