Sentiment analysis of Twitter data by making use of SVM, Random Forest and Decision Tree algorithm

Data production over the internet has been increased tremendously because of social media sites' growth. A significant number of users share their thoughts, images, videos etc., on their particular accounts. Among these, all varieties available for the thoughts expression twitter provide a concise way to share users' thoughts. This thought shared by users is called tweets. These tweets may lead to a big revolution for a good change or, at the same time, lead to a big issue over any particular topic. So sentiments involved in such tweets needed to be classified, and then only the tweet may be shared with other users. Here in our paper, we have taken a dataset from the Kaggle website, which was collected based on KFC's challenge and McDonald's of AI. We have used more than 14000 tweets for analysis. The cleaning of the dataset is done using Term Frequency- Inverse Document Frequency (TF-IDF). After cleaning the dataset, we have applied three classification algorithms for the testing purpose that are Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT). Among these three algorithms, the highest accuracy is obtained from the Decision tree algorithm is 88.51%. The experiment calculates accuracy, recall, precision and F1 measures.