Sentiment Analysis Using NLP & Deep Learning on Social Media Tweets

A form of natural language processing (NLP) method that determines the positivity, negativity, or neutrality of data. Frequently, sentiment assessment is carried out on written information. The present research seeks to conduct sentiment analysis upon social media messages through NLP (natural language processing) along with advanced learning methods. The objective is to correctly categories tweets to be either beneficial or detrimental according to the tone of the content. The study employs methodologies for preprocessing that include tokenization, stop-word elimination, along with stemming to analyse a dataset comprised of tweets collected from different social networks. The previously prepared information will be utilized to train a model determined by deep learning, and specifically the Random Forest or Supporting raster machine-based algorithm and LSTM, to categories tweets through sentiment classifications. The efficiency of the framework will be assessed employing metrics that include precision, recall, and accuracy. This project's outcomes could possess significant consequences for companies and organizations that depend on social networking sites for advertising and managing their brands. By analysing the sentiment about tweets about their goods or services, businesses can obtain valuable insights into their customers' opinions as well as preferences, that may influence their advertising tactics and increase customer satisfaction.