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首页> 外文会议>International Seminar on Application for Technology of Information and Communication >Prediction of Movie Sentiment Based on Reviews and Score on Rotten Tomatoes Using SentiWordnet
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Prediction of Movie Sentiment Based on Reviews and Score on Rotten Tomatoes Using SentiWordnet

机译：使用SentiWordNet的腐烂西红柿的评论和得分预测电影情绪

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摘要

With the number of films released each year, the movie review website is becoming more popular. One of the most referenced movie review websites is Rotten Tomatoes. Rotten Tomatoes recommend films based on their Tomatometer. Tomatometer represents the percentage of professional critic reviews that are positive or negative for a given film or television show. While fresh reviews represent positive sentiment, rotten reviews mean that the movie critics give the movie negative sentiments. Unfortunately, the method to determine the given score is not available to the public. Thus, the public does not know which parameter affect the prediction of the sentiment. This paper proposes a new method to predict the sentiment of the movie on the rotten tomatoes by combining the sentiment score from SentiWordnet and expert original score. the result of the experiment shows that the proposed method gives better F measure compared to those of the other methods with the value of 0.97.
机译：随着每年发布的电影数量，电影审查网站正在变得越来越受欢迎。其中一个最引用的电影评论网站是腐烂的西红柿。腐烂的西红柿推荐基于他们的观光仪的电影。 tomatometer代表了专业评论家评论的百分比，对给定的电影或电视节目是积极的或消极的。虽然新鲜审查代表了积极的情绪，但腐烂的评论意味着电影批评者给电影负面情绪。不幸的是，公众不可用的方法确定给定分数。因此，公众不知道哪个参数会影响对情绪的预测。本文提出了一种新的方法，通过将来自Sentiwordnet和专家原始分数的情感分数组合来提出了一种预测腐烂的西红柿的情绪。实验结果表明，与值为0.97的其他方法相比，该方法提供了更好的F测量。

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