Amrita-CEN-SentiDB: Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Abstract

Social media is a platform in which the data is generated each and every day in an abundance manner. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, we use the text data for classification of tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are common between humans which has a way to express it that decides the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) that belongs to machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. The dataset is made publically available for research purpose.