论文标题
电影票房通过联合演员代表和社交媒体情绪的预测
Movie Box office Prediction via Joint Actor Representations and Social Media Sentiment
论文作者
论文摘要
近年来，在亚洲电影业（例如中国和印度）的推动下，全球票房一直保持着稳定的增长趋势。先前的研究很少在分析中使用长期的全样电影数据，缺乏对参与者的社交网络的研究。现有的电影票房预测算法仅使用胶片元数据，缺乏使用社交网络特征，而模型则较不容易解释。我在票房预测任务中提出了一个FC-GRU-CNN二进制分类模型，结合了五个特征，包括电影元数据，Sina Weibo文本情感，演员的社交网络测量，所有配对最短的路径和演员的艺术贡献。在长序列中利用GRU层的长期记忆能力以及CNN层在检索所有最短路径矩阵特征中的映射能力，精度的准确性比当前最佳C-LSTM模型高14％。
In recent years, driven by the Asian film industry, such as China and India, the global box office has maintained a steady growth trend. Previous studies have rarely used long-term, full-sample film data in analysis, lack of research on actors' social networks. Existing film box office prediction algorithms only use film meta-data, lack of using social network characteristics and the model is less interpretable. I propose a FC-GRU-CNN binary classification model in of box office prediction task, combining five characteristics, including the film meta-data, Sina Weibo text sentiment, actors' social network measurement, all pairs shortest path and actors' art contribution. Exploiting long-term memory ability of GRU layer in long sequences and the mapping ability of CNN layer in retrieving all pairs shortest path matrix features, proposed model is 14% higher in accuracy than the current best C-LSTM model.
