Deep Learning Approach to Predicting the Success of Content

Metadata

Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2020, No. 11, pp. 1–12
Abstract: Movie Industry across the globe is a multi-billion dollar in revenue. There are several factors and somewhat complicated, which makes a movie a box-office success. The financial cost of making a movie [1] can range from modest cost to mammoth several 100 million dollars in spending. The audience rules in terms of liking/disliking of a movie. Financiers and producers reel through several financial losses if the movie does not do well in the box-office. Can Artificial Intelligence techniques help from historical data, be it movies, audience reviews/sentiments, cast/crews to predict the success of a movie? Our paper explores the proposal for a hybrid RNN/LSTM/CNN based neural network architecture that can predict the success of the content. It leads to a useful foundation that enables content creators to manage the pre-production part of the journey better by making the right decisions that enhance the probability of success. The foundation of our proposal can help the pre-production state with a certain degree of predictability to the success of the content, explainability causing the success, and the next best action.
Publication Date: 2020-11-10
DOI: 10.5594/M001914
Link: https://doi.org/10.5594/M001914
Author(s): Suman KalyanData Scientist, Ness Digital Engineering
Ashwyn TirkeySolution Architect, Ness Digital Engineering
Angshuman PatraSVP Engineering, Ness Digital Engineering
Sujay KumarVP of Business Development, Ness Digital Engineering
Pranav SinghAVP Business Development, Ness Digital Engineering
Abraham AddankiData Scientist, Ness Digital Engineering
Keyword(s): CNN, LSTM, RNN, Motion Pictures, Posters, Video, Genres, Explainability
Copyright: © 2020 Society of Motion Picture and Television Engineers, Inc.

Source Data (JSON)

Full registry record with provenance metadata. Open directly: /api/doc/10.5594-M001914.json

Reference Tree

Explore all references and references to this document, as a navigable tree.

Open Reference Tree

Reference this Doc

Plain text (ISO 690 compliant)

Preview:

Suman Kalyan, Ashwyn Tirkey, Angshuman Patra, Sujay Kumar, Pranav Singh, and Abraham Addanki; Deep Learning Approach to Predicting the Success of Content, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at https://doi.org/10.5594/M001914

Snippet:

Suman Kalyan, Ashwyn Tirkey, Angshuman Patra, Sujay Kumar, Pranav Singh, and Abraham Addanki; Deep Learning Approach to Predicting the Success of Content, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at https://doi.org/10.5594/M001914

HTML (ISO 690 compliant)

Preview:

Snippet:

<span class="citation">Suman Kalyan, Ashwyn Tirkey, Angshuman Patra, Sujay Kumar, Pranav Singh, and Abraham Addanki; <cite>Deep Learning Approach to Predicting the Success of Content</cite>, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at <a href="https://doi.org/10.5594/M001914" target="_blank" rel="noopener">https://doi.org/10.5594/M001914</a></span>

SMPTE's HTML Pub

Preview:

Snippet:

<li>
Suman Kalyan, Ashwyn Tirkey, Angshuman Patra, Sujay Kumar, Pranav Singh, and Abraham Addanki; <cite id="bib-10-5594-m001914">Deep Learning Approach to Predicting the Success of Content</cite>, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020
<span class="doi">10.5594/M001914</span>
</li>

Referenced By

Model-based Predictive Control for Continuous Success Planning in Movie Production [Active]