Quantum-inspired framework for big data analytics: evaluating the impact of movie trailers and its financial returns
  • Singh, Jaiteg
  • Bhangu, Kamalpreet Singh
  • Ali, Farman
  • Alzubi, Ahmad Ali
  • Shah, Babar
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초록

In the context of the growing influence of businesses and marketers on social media platforms, understanding the impact of emotionally charged content on consumer behavior has become increasingly crucial. This study proposes a novel framework, leveraging quantum computing principles, to assess the emotional impact of movie trailers. The framework incorporates big data analytics and utilizes Quantum Walk andQuantum Time Series models to investigate the relationship between a movie trailer's emotional intensity and its financial performance. Unlike sequential problem-solving approach of traditional computing models, Quantum superposition allows exploring multiple options at once. An analysis of 141 movie trailers released after January 1, 2022, revealed a positive correlation between a trailer's emotive score and its financial success. These findings suggest that trailers evoking a stronger emotional response tend to achieve greater box office returns compared to those with a lower emotional impact. This research underscores the pivotal role of emotionally resonant content in shaping consumer behavior and cinematic outcomes. It would offer valuable insights for filmmakers and marketers to optimize audience engagement and financial returns.

키워드

Quantum computingBig data analyticsFama-French modelMovie trailer reviewsFinancial returnsFACIAL EXPRESSION RECOGNITIONBOX-OFFICE REVENUEMOTION-PICTURESSUCCESSPREDICTIONS
제목
Quantum-inspired framework for big data analytics: evaluating the impact of movie trailers and its financial returns
저자
Singh, JaitegBhangu, Kamalpreet SinghAli, FarmanAlzubi, Ahmad AliShah, Babar
DOI
10.1186/s40537-025-01069-x
발행일
2025-02-01
유형
Article
저널명
JOURNAL OF BIG DATA
12
1