Sentimental Analysis using Dictionary and Machine Learning Approach
Manish Dwivedi
Department of Computer Science and Engineering
GL Bajaj Institute of Technology and Management(GLBITM)
Greater Noida
manishclg640@gmail.com
Ishika Chaudhary
Department of Computer Science and Engineering
GL Bajaj Institute of Technology and Management(GLBITM)
Greater Noida
chaudharyishika67@gmail.com
Satya Prakash Yadav
Department of Computer Science and Engineering
GL Bajaj Institute of Technology and Management(GLBITM)
Greater Noida
Satya.yadav_cse@glbitm.ac.in
Sansar Singh Chauhan
Department of Computer Science and Engineering
GL Bajaj Institute of Technology and Management(GLBITM)
Greater Noida
hod.cse@glbitm.ac.in
Abstract— Nowadays we can see a significant surge in user-generated material on the web as a result of enhanced digitization, which gives people's thoughts on many themes. The computer study of assessing people's sentiments and views regarding an entity is known as sentiment analysis. What do people think? How do you feel about a specific topic? Bringing together computer science researchers, Computing linguistics, data mining, psychology, and even sociology are just a few examples. Sentiment analysis is a text mining approach that automatically analyses text for the writer's sentiment using machine learning and natural language processing (NLP) (positive, negative, neutral, and beyond). Positive, Negative, and Neutral comments may be readily identified using powerful machine learning algorithms. There are various types of sentimental analysis. The main feature of emotional is that it classifies the polarity in text data. The number of smart phones is expanding in lockstep with the growth of the internet. The contemporary Internet allows millions of individuals all over the world to connect with one another and share their ideas and opinions via email, social networking websites like Twitter, Facebook and other means. It is the cheapest and most convenient method of interacting with others. There is a lot of text material on this social networking site. These text data may be utilized to analyse public sentiment on certain issues, as well as the emotion exhibited on any online platform. In this paper we are going to review and compare the Traditional Dictionary Based Approach and Machine Learning with text classifier which is trained with the dataset of U.S. Airline under first propose and second propose.
Keywords: Data Pre-Processing, sentiment analysis, NLTK, matplotlib, Long-short-Term-Memory (LSTM), binary text classifier, Pandas, TensorFlow.
