Advertisement

M2P2: Movie’s Trailer Reviews Based Movie Popularity Prediction System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1053)

Abstract

In today’s world, knowing which movie will be popular among the masses, motivates movie’s crew, before movie comes on the PVR curtain. Trailers and teasers usually come out, before releasing any movie. Moreover, the people are giving comments or feedback about the movie based on the trailer or teaser. These reviews or comments may be used to predict the popularity of the movie in the near future. Therefore, this work focuses for predicting the popularity or success of the movie based on analysis of the trailer comments. This work exposes the importance of the TextBlob and Enchant library. In addition to this, dictionary-based recommendation system has been developed in order to enhance the accuracy of the proposed popularity prediction system. For experimental process, the reviews of the people on trailers before release of the movie have been used to extract the features, and then the same features are mapped on corresponding BookMyShow popularity index after the movie has been released. A percentage of accuracy of the model shows that the proposed model can be used as an accurate movie recommendation system.

Keywords

Movie Review Popularity Dictionary Trailer 

References

  1. 1.
    Johari, et al.: Bone fracture detection using edge detection technique. SoCTA 584 (2018)Google Scholar
  2. 2.
    Singh, et al.: Application of unnormalized and phase correlation techniques on infrared images. SoCTA 584 (2018)Google Scholar
  3. 3.
    Aditya, et al.: STAR: rating of reviewS by exploiting variation in emoTions using trAnsfer leaRning framework. CICT (2018)Google Scholar
  4. 4.
    https://towardsdatascience.com. Accessed 16 Jan 2018
  5. 5.
    Singh, et al.: Study and analysis of different kinds of data samples of vehicles for classification by bag of feature technique. SoCTA 742 (2019)Google Scholar
  6. 6.
    Kumar, et al.: Eratosthenes sieve based key-frame extraction technique for event summarization in videos. MTAP (2017)Google Scholar
  7. 7.
    https://textblob.readthedocs.io/. Accessed 15 May 2018
  8. 8.
    Library, E.: https://abiword.github.io/enchant/. Accessed 18 July 2018
  9. 9.
    Kumar, et al.: Sentimentalizer: docker container utility over Cloud. ICAPR (2017)Google Scholar
  10. 10.
    Liu, B.: Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5(1), 1–167 (2012)CrossRefGoogle Scholar
  11. 11.
    Jindal, N., Liu, B.: Mining comparative sentences and relations. AAAI 22, 1331–1336 (2006)Google Scholar
  12. 12.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. ACM SIGKDD, 168–177 (2004)Google Scholar
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Kumar, K., et al.: F-DES: Fast and Deep Event Summarization. IEEE TMM (2017)Google Scholar
  17. 17.
    Krishan, et al.: ESUMM: event SUMMarization on scale-free network. TITR (2018)Google Scholar
  18. 18.
    Shikhar, et al.: LEXER: LEXicon based Emotion analyzeR. PReMI (2017)Google Scholar
  19. 19.
    Esuli, et al.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation, 1–26 (2007)Google Scholar
  20. 20.
    https://medium.com/@ageitgey/. Accessed 20 April 2018
  21. 21.
    Haroon, et al.: DCR-HMM: depression detection based on content rating using hidden markov Model. CICT (2018)Google Scholar
  22. 22.
    Kumar, S., et al.: IRSC: integrated automated review mining system using virtual machines in cloud environment. CICT (2018)Google Scholar
  23. 23.
    www.liwc.net. Accessed 18 May 2018
  24. 24.
    Shubham, et al.: SRC: lexicon star ratings system over Cloud. RAIT (2018)Google Scholar
  25. 25.
  26. 26.
    Harman, et al.: HDML : habit detection with machine learning. ICCCT (2017)Google Scholar
  27. 27.
    Krishan, et al.: D-CAD: deep and crowded anomaly detection. ICCCT (2017)Google Scholar
  28. 28.
    Shikhar, et al.: D-FES: deep facial expression recognition system. CICT (2017)Google Scholar
  29. 29.
    Kumar, et al.: Deep event learning boosT-up approach: DELTA. MTAP (2018)Google Scholar
  30. 30.
    Akkaya, et al.: Subjectivity word sense disambiguation. EMNLP (2009) Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology, Uttarakhand SrinagarGarhwalIndia
  2. 2.Motilal Nehru National Institute of TechnologyAllahabadIndia

Personalised recommendations