基于微博的电影首映周票房预测建模

Predicting Opening Weekend Box Office Prediction Based on Microblog

【目的】解决现有的票房预测模型由于数据受限等因素导致的无法实现在影片上映前进行票房预测这一问题。【方法】在获取微博评论的基础上,使用SVM识别出消费者的显式消费意图,即强正面评论;对传统的分类准则进行修正,构建基于How Net的中文微博情感词典,进而定义一个新的用户影响力特征;使用BP神经网络进行票房预测。【结果】实验结果表明,本文建立的模型能够较为准确地对电影首映周票房进行预测。【局限】由于语料不充分,本文构建的中文微博情感词典,可能会无法在所有的电影微博评论中表现出较好的分类效果;此外也没有建立一个能够在电影上映周期内动态预测票房的票房预测模型。【结论】该模型能够有效地进行首映周票房预测,具有现实的可行意义。

[Objective] This study aims to solve the problems of the existing pre-release box office prediction models due to data constraints and other factors. [Methods] We first retrieved microblog comments, and then used SVM to identify explicit consumer intention, namely strong positive comments. Second, we modified the traditional sentiment classification schemes to build a Chinese microblog sentiment dictionary based on How Net. Finally, we defined a new user influence feature and used the BP neural network to predict box office. [Results] The proposed model could forecast the opening box office more accuately. [Limitations] Due to inadequate corpus, the sentiment dictionary may not work well for all microblog movie comments. A dynamic forecasting model was not established between the pre-release and post-release period. [Conclusions] The proposed model can effectively predict opening box office.