Movie Success-Rate Prediction System through Optimal Sentiment Analysis

Kemal Polat

¹Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari 61100, Pakistan

²School of Computer Science and Engineering, Central South University, Changsha, 410083, China

³Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey

^*Corresponding Author: Kemal Polat, Email: [email protected]

With the speedy growth of social media, it has become easy for people to express their feelings about anything and give their opinions. These opinions are helpful in business plans development, marketing trends, political parties’ popularity. Different social media sources are used for this purpose, i.e., Facebook, Instagram, YouTube, Twitter, etc. The rapid growth of text data on social media is required to develop algorithms and techniques for recognizing people’s opinions towards a specific subject. Nowadays, Twitter becomes a rapidly used social media application where people feel free to share their feelings about anything and give their opinions. Film Industry is one of the revenue-generating Industries for the economic growth of any country. People express their views about any upcoming movie by watching its trailer using social media. The practical sentiment analysis of opinions on social media such as Twitter can be helpful to predict movie ratings. This research focused on developing a technique to predict movie success rates based on viewers’ tweets on movie trailers. The results provide the movie rating in the star’s form (1-5). We have collected tweets about different movies after their trailer was released by applying the hashtag method (#Hash). We have used four key algorithms (Naïve Bayes, SVM, decision tree, and KNN) on NLTK Movie review corpora and train & test our models. Machine learning training data sets were not readily available for movies ratting; then, we shifted towards a lexicon-based approach. All these three dictionaries have a different word count, and each word in these dictionaries has its own polarity in the form of a score. Finally, we have also compared our results with other movie rating sites like IMDB rating, which are satisfactory.

Optimal Sentiment Analysis, Machine Learning, Analysis, Decision Making

Dewan Muhammad Qaseem, Nashit Ali, Waseem Akram, Aman Ullah, Kemal Polat (2022). Movie Success-Rate Prediction System through Optimal Sentiment Analysis. Journal of the Institute of Electronics and Computer, 4, 15-33. https://doi.org/10.33969/JIEC.2022.41002.

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