Sentiment Analysis to Predict Movies Success Rate Based on NLTK Movie Review Corpora Using Machine Learning

Author:

Muhammad Luqman 1,Amir Yaqoob 2,Majid Bashir Ahmad 2,Kanza Majid 3

Affiliation:

1. Department of Computer Science, University of Gujrat, Gujrat, Pakistan

2. Department of computer science, University of Lahore, Lahore, Punjab, Pakistan

3. Department of computer science, National College of Business Administration & Economics, Lahore, Punjab, Pakistan

Abstract

With the proliferation of social networks, peoples express their opinions about different things or issues on social media without any hesitation. The rapid growth of textual data on social media are required to develop algorithms and techniques for recognizing people’s opinions towards specific subject. These opinions are helpful in business plans development, marketing trends, political parties’ popularity. The film industry can be an important revenue generating industry of any country. Peoples express their opinion on movie trailer using social media. The effective sentiment analysis of opinions on social media such as Twitter can be helpful to predict movie ratings. This research work focuses on developing a technique to predict movie success rate on the basis of tweets data. We have collected tweets about different movies after their trailer released by using hash tag method. We applied Sentiment analysis approach using Machine learning. In this study we utilized four key algorithms (Naïve Bayes, SVM, Neural Networks, decision tree) on NLTK Movie review corpora.

Publisher

Technoscience Academy

Subject

General Medicine

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Elevating Movie Reviews with Intelligent Recommendation Systems;2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI);2023-12-21

2. Enhancing Sentiment Analysis Accuracy on IMDB Reviews Through Ensemble Machine Learning Techniques;2023 IEEE 21st Jubilee International Symposium on Intelligent Systems and Informatics (SISY);2023-09-21

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