Machine Learning and Rule-Based Approaches used for Sentiment Analysis Compared the Performance of the Classification Models in Customer Analysis using NLP

Author Name : Venu Majji, Vanitha Kakollu

ABSTRACT

Sentiment analysis is the study of Natural Language processing techniques from artificial intelligence which are used to analyze and predict the user emotions and also know positive and negative data. We use machine learning & deep-learning libraries or models for this process. It is one of the most emerging research area in the field of artificial intelligence. In this approach we perform text mining and data analysis in order to know the human sentiments. It is very time taking process to analyze a large number of data set manually in real time. By using sentiment analysis models we perform the above process easily and in efficient manner. Sentiment analysis on the raw text maybe a very complicated task thanks to various reasons like a sarcastic text or positive and negative sentiment utilized in an equivalent text. Using machine learning algorithms like logistic regression and naive Bayes classification over a cleaned and processed data.

Keywords: Machine Learning, Natural Language Processing (NLP), supervised machine learning algorithm, logistic regression, naive bayes classification.