使用字幕的印度电影早期成功预测:一个文件向量方法

Vaddadi Sai Rahul, M. Tejas, N. Prasanth, S. Raja
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引用次数: 0

摘要

对影响印度电影票房表现的因素的科学研究通常集中在后期制作因素上,例如在电影完成或发行后发现的因素,尤其是宝莱坞电影。只有很少的研究关注了地区电影工业和制作前因素,这些因素是在决定拍摄一部电影之前就知道的。这项研究使用自然语言处理和机器学习方法来研究印度电影,看看它们在前期制作阶段是否有利可图。我们提取了印度五大地区电影行业的电影数据和英文字幕(近似于剧本):宝莱坞、Kollywood、Tollywood、Mollywood和檀香木,因为它们构成了印度电影行业收入的主要部分。Subtitle Vector (Sub2Vec)是一种基于英文字幕训练的段落向量模型,用于将字幕文本嵌入到50和100个维度。拟议的方法遵循两个阶段的管道。在第一阶段,使用聚合的字幕嵌入和相关的电影数据计算投资回报率(ROI)。分类模型使用第一步计算的ROI来预测第二步的电影判决。通过使用[公式:见文本]-score和Cohen 's Kappa分数对各种超参数评估分类模型来确定最佳回归器-分类器对。与基准方法相比,我们提出的方法更准确地预测票房成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Success Prediction of Indian Movies Using Subtitles: A Document Vector Approach
Scientific studies of the elements that influence the box office performance of Indian films have generally concentrated on post-production elements, such as those discovered after a film has been completed or released, and notably for Bollywood films. Only fewer studies have looked at regional film industries and pre-production factors, which are elements that are known before a decision to greenlight a film is made. This study looked at Indian films using natural language processing and machine learning approaches to see if they would be profitable in the pre-production stage. We extract movie data and English subtitles (as an approximation to the screenplay) for the top five Indian regional film industries: Bollywood, Kollywood, Tollywood, Mollywood, and Sandalwood, as they make up a major portion of the Indian film industry’s revenue. Subtitle Vector (Sub2Vec), a Paragraph Vector model trained on English subtitles, was used to embed subtitle text into 50 and 100 dimensions. The proposed approach followed a two-stage pipeline. In the first stage, Return on Investment (ROI) was calculated using aggregated subtitle embeddings and associated movie data. Classification models used the ROI calculated in the first step to predicting a film’s verdict in the second step. The optimal regressor–classifier pair was determined by evaluating classification models using [Formula: see text]-score and Cohen’s Kappa scores on various hyperparameters. When compared to benchmark methods, our proposed methodology forecasts box office success more accurately.
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