Project Description :
This project presents an empirical study of efficiency of machine learning techniques in classifying text by text mining. It aims to analyze people’s sentiments, opinions, and emotions towards movie review. Everyone knows the existence of internet and its huge advantages to our daily life. So it does on movie review websites, where reviewers can post anything that they feel about the movie they have watched or want to watch. However, due to the increasing numbers of Internet users, the reviews can be overwhelming. In movie industries, directors can be having difficulties in time or even patience for analysing reviews, due to unhandled amount of them. This research proposed to help publication team in making ease for them to analyze the review in order to increase movie rating in future work. The method will be using supervised learning, SVM, with the help of available corpus from the previous works in the internet using Python language-based. SVM was found have better accuracy than other machine learning in supervised learning method. The system that will be developed will be able to classify the review into positive or negative sentiment with the polarity of the review. At the end of this, the goal of this research is to have a comparison of movie reviews by genre to the production team, to enable them to plan carefully for the next production to be success after they see which genre audience actually liked or dislike the most.
Research/Project Team :
- Pn Sofianita Mutalib ( Project Leader )
- Muhammad Naufal Harraz Bin Abdul Halim
Contact Person :
Pn Sofianita Mutalib ( sofi@tmsk.uitm.edu.my )