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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
https://in.bookmyshow.com/[Accessed on 15 May, 2018].
References
Johari, et al.: Bone fracture detection using edge detection technique. SoCTA 584 (2018)
Singh, et al.: Application of unnormalized and phase correlation techniques on infrared images. SoCTA 584 (2018)
Aditya, et al.: STAR: rating of reviewS by exploiting variation in emoTions using trAnsfer leaRning framework. CICT (2018)
https://towardsdatascience.com. Accessed 16 Jan 2018
Singh, et al.: Study and analysis of different kinds of data samples of vehicles for classification by bag of feature technique. SoCTA 742 (2019)
Kumar, et al.: Eratosthenes sieve based key-frame extraction technique for event summarization in videos. MTAP (2017)
https://textblob.readthedocs.io/. Accessed 15 May 2018
Library, E.: https://abiword.github.io/enchant/. Accessed 18 July 2018
Kumar, et al.: Sentimentalizer: docker container utility over Cloud. ICAPR (2017)
Liu, B.: Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5(1), 1–167 (2012)
Jindal, N., Liu, B.: Mining comparative sentences and relations. AAAI 22, 1331–1336 (2006)
Hu, M., Liu, B.: Mining and summarizing customer reviews. ACM SIGKDD, 168–177 (2004)
https://www.oreilly.com/ideas/inside-the-washington-posts-popularity-prediction-experiment. Accessed 18 July 2018
https://arxiv.org/pdf/1603.09436.pdf. Accessed 18 July 2018
https://arxiv.org/pdf/1510.06223.pdf. Accessed 10 Aug 2018
Kumar, K., et al.: F-DES: Fast and Deep Event Summarization. IEEE TMM (2017)
Krishan, et al.: ESUMM: event SUMMarization on scale-free network. TITR (2018)
Shikhar, et al.: LEXER: LEXicon based Emotion analyzeR. PReMI (2017)
Esuli, et al.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation, 1–26 (2007)
https://medium.com/@ageitgey/. Accessed 20 April 2018
Haroon, et al.: DCR-HMM: depression detection based on content rating using hidden markov Model. CICT (2018)
Kumar, S., et al.: IRSC: integrated automated review mining system using virtual machines in cloud environment. CICT (2018)
www.liwc.net. Accessed 18 May 2018
Shubham, et al.: SRC: lexicon star ratings system over Cloud. RAIT (2018)
http://www.statisticshowto.com/rmse/. Accessed 30 June 2018
Harman, et al.: HDML : habit detection with machine learning. ICCCT (2017)
Krishan, et al.: D-CAD: deep and crowded anomaly detection. ICCCT (2017)
Shikhar, et al.: D-FES: deep facial expression recognition system. CICT (2017)
Kumar, et al.: Deep event learning boosT-up approach: DELTA. MTAP (2018)
Akkaya, et al.: Subjectivity word sense disambiguation. EMNLP (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kumar, K., Bamrara, R., Gupta, P., Singh, N. (2020). M2P2: Movie’s Trailer Reviews Based Movie Popularity Prediction System. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_62
Download citation
DOI: https://doi.org/10.1007/978-981-15-0751-9_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0750-2
Online ISBN: 978-981-15-0751-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)