Analyzing national film based on social media tweets input using topic modelling and data mining approach
College
College of Computer Studies
Department/Unit
Information Technology
Document Type
Conference Proceeding
Source Title
Lecture Notes in Electrical Engineering
Volume
481
First Page
379
Last Page
389
Publication Date
1-1-2019
Abstract
This paper presents a methodology to measure and analyze mass opinion towards underdeveloped forms of art such as independent films using data mining and topic modelling approach, rather than limited sampling through traditional movie revenue and surveys. Independent films are cultural mediums that foster awareness and social transformation through the advocacies and social realities they present. This methodology helps in addressing challenges of film stakeholders and cultural policy-making bodies in assessing cultural significance of independent films. Twitter has allowed innovative methods in data mining to understand trends and patterns that provide valuable support in decision making to domain experts. By determining the status of Philippine Cinema using social media data analytics as the primary source of the collective response, film stakeholders will be provided with a better understanding how the audience currently interprets their films with results as quantitative evidence. We use the tweets from the Pista ng Pelikulang Pilipino given the festival objective of showcasing films that enhances “quality of life, examine the human and social condition, and contribute to the nobility and dignity of the human spirit”. © Springer Nature Singapore Pte Ltd. 2019.
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Digitial Object Identifier (DOI)
10.1007/978-981-13-2622-6_37
Recommended Citation
Ramos, C. L., Suarez, M. C., & Tighe, E. P. (2019). Analyzing national film based on social media tweets input using topic modelling and data mining approach. Lecture Notes in Electrical Engineering, 481, 379-389. https://doi.org/10.1007/978-981-13-2622-6_37
Disciplines
Arts and Humanities
Keywords
Independent films—Philippines—Public opinion; Twitter; Sentiment analysis
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