Efficient NLP-Based Framework for Tweets Sentiment Analysis via Advanced Machine Learning Model

Sentiment analysis is the most well-known and actively researched area in data mining. Among the many new kinds of online social interaction, Twitter has emerged as an indispensable tool for communicating and collecting opinions on a huge range of topics. Because of this, sentiment analysis gained interest in the area of NLP. Traditional ways of sentiment classification are not very effective in terms of both accuracy and context relevance. To reference these limitations, the following is a breakdown of the improved sentiment analysis model that uses news data based on the BiLSTM algorithm on the Sentiment140 data set. The proposed Biuctive LSTM model significantly outperformed the other machine learning models like the Naïve Bayes (76.57% accuracy) and Logistic Regression (accurate74%) with an 81% accuracy, which was achieved after optimization that entailed processes like stop-word removal, stemming, and feature extraction by Bag-of-Words (BoW). The model shows high accuracy in the sentiment classification since a recall of 80% and precision of 82% is realized. This denotes a high level of generalization with minimal instances of overtraining for the training and validation group. The result proved that BiLSTM is superior in dealing with serious complex sentiment patterns as a model of real-time SA especially on social media applications.