An Integrated Framework for Sentiment Analysis of Giant Cold Drinks Companies

Twitter is an open, freely available and widely used social media platform for sharing emotions, political and social opinions without hesitation. With the passage of time data generated by Twitter is increasing tremendously and due to availability of the such a huge amount of data, there is an opportunity for industry giants to analyze their current product reviews and made a better business making for the future and to conformance needs of customers. Similarly, there is an opportunity for research aspirants to explore the data to provide a better solution for modern problems. Therefore, in this research paper, we perform a detailed sentiment analysis of the three beverage giants (Pepsi Vs Fanta Vs Coca-cola). Specifically, we perform the classification of extracted tweeter data into three classes (positive, negative and neutral) then the visualization of the data is performed and at the end, naïve Bayes, decision tree and LSTM algorithm is applied to perform the prediction of tweets. Particularly, we use machine learning libraries and natural language toolkits for data extraction, data visualization, sentiment analysis and prediction task. The maximum accuracy obtained on Pepsi, Fanta and Coca-Cola is 89%, 92% and 91% respectively by applying the LSTM algorithm. The experimental result reveals that LSTM is a better technique for continuous data