Predicting Tags for Movies from Plot Synopses Using Deep Learning Techniques
Date
2019-11-15
Authors
Journal Title
Journal ISSN
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Publisher
Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh
Abstract
Automatically generating or predicting tags for movies can help recommendation
engines improve retrieval of similar movies, and help viewers know what to expect
from a movie in advance. It improves the search results of a movie recommender
system by predicting high weighted tags from a movie's plot synopsis. We propose
a model in which we at rst perform pre-processing of data(stopwords eradication,
stemming of data etc.) and then tokenize the data by a technique called BERT
and then vectorize it by TF-IDF process and then input those pre-processed data
to a deep learning technique to give us a prediction tag scores from a set of tags
for movies. We compare our system's result with an already proposed model
with emotion
ow encoded neural network and found that our model's perfor-
mance shows improvement in result(TL, TR and F1 measure) specially due to
pre-processing of data and for using the techniques like BERT and TF-IDF.
Description
Supervised by Mr. Raihan Islam Arnob
Keywords
Citation
[1] Sudipta Kar, Suraj Maharjan, Thamar Solorio. August 20-26, 2018. Folkso- nomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network. Proceedings of the 27th International Confer- ence on Computational Linguistics, pages 2879{2891 Santa Fe, New Mexico, USA. [2] Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Google AI Language. [3] Rajaraman, Anand and Ullman, Je rey David. 2011. Mining of Massive Datasets. Cambridge University Press, New York, NY, USA. [4] Jie Kang, Kyle Condi , Shuo Chang, Joseph A. Konstan, Loren Terveen, and F.Maxwell Harper. 2017. Understanding How People Use Natural Language to Ask for Recommendations. In Proceedings of RecSys '17, Como, Italy, August 27-31, 2017, 9 pages. [5] Bird, Steven, Edward Loper and Ewan Klein. 2009. Natural Language Pro- cessing with Python. O'Reilly Media Inc. [6] DataFlair Team. 2019. Machine Learning Project { Data Science Movie Rec- ommendation System Project in R. https://data-flair.training/blogs/ data-science-r-movie-recommendation/ [7] Tony Yiu. 2019. Understanding Neural Networks. https:// towardsdatascience.com/understanding-neural-networks-19020b758230 [8] Judit Acs's blog. http://juditacs.github.io/2019/02/19/ bert-tokenization-stats.html [9] Aditi Mittal. Understanding RNN and LSTM. https:// towardsdatascience.com/understanding-rnn-and-lstm-f7cdf6dfc14e 42 [10] Kunal Gupta. Predicting Movie Genres Based on Plot Summaries. https://medium.com/@kunalgupta4595/ predicting-movie-genres-based-on-plot-summaries-bae646e70e04 [11] Sudipta Kar, Suraj Maharjan, A. Pastor Lopez-Monroy and Thamar Solorio. 2018. MPST: A Corpus of Movie Plot Synopses with Tags. 43