Amrita-CEN-Senti-DB:Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning
2020
Social media is a platform in which tons and tons of
text are generated each and every day. 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, the text data
which is used for the classification is tweets that determines the state of the
person according of the sentiments which is positive, negative and neutral.
Emotions are the way of expression of 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 the purpose of the 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) which is also one of the
machine learning algorithm out performs well making a benchmark accuracy for
this dataset as the 75.8%. For the research purpose the dataset has been made publically
available.
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