Sentiment Analysis on Tweets with Deep Learning and Natural Language Processing

Today’s social media users are very large, where everyone expresses opinions, comments, criticisms, and so on. The data provides valuable information to be able to help people or groups in opinion evaluation. The enormous quantity of information is impractical for humans to interpret and evaluate manually. The Sentiment Analysis (SA) is a task that has become very important for the scientific and business community. Thus, SA can be used to diagnose a population regarding specific issues. There are many machine learning techniques, Deep Learning (DL) is one of them that has a deeper architecture than other ML techniques in solving prediction and classification problems. Using DL Models and NLP, this work tries to categorise content on social media into positive, negative, and neutral groups as sentiments. While NLP is being used to extract features, which enhances accuracy and performance, other DL models for classification, such as CNN, RNN, and LSTM models, can synthesize or create acceptable sentiments with a voluminous data; however, they take longer to train.