论文标题
基于DL的电影评论分析
DL based analysis of movie reviews
论文作者
论文摘要
毫无疑问，社交媒体通过以各种语言和成语表达的大量故事，反馈，评论和反应进行了集思广益，即使有些人实际上是不正确的。这些主题使评估此类数据具有挑战性，耗时，并且容易受到误解的影响。本文介绍了建立在深度学习方法上的电影评论的分类模型。使用IMDB电影评论数据库中的几乎500kb的平衡数据来训练该模型。人们对电影的观点是使用长期短期记忆（LSTM）和卷积神经网络（CNN）策略进行了分类的。根据研究结果，CNN算法的预测准确率将接近97.4％。此外，由LSTM训练的模型导致了周围的准确性，并在Keras库中施加99.2％。通过修改模型参数对模型进行了更多研究。根据结果，LTSM在评估IMDB电影评论方面的表现优于CNN，并且计算上的成本低于LSTM。
Undoubtedly, social media are brainstormed by a tremendous volume of stories, feedback, reviews, and reactions expressed in various languages and idioms, even though some are factually incorrect. These motifs make assessing such data challenging, time-consuming, and vulnerable to misinterpretation. This paper describes a classification model for movie reviews founded on deep learning approaches. Almost 500KB pairs of balanced data from the IMDb movie review databases are employed to train the model. People's perspectives regarding movies were classified using both the long short-term memory (LSTM) and convolutional neural network (CNN) strategies. According to the findings, the CNN algorithm's prediction accuracy rate would be almost 97.4%. Furthermore, the model trained by LSTM resulted in accuracies of around and applying 99.2% within the Keras library. The model is investigated more by modification of model parameters. According to the outcomes, LTSM outperforms CNN in assessing IMDb movie reviews and is computationally less costly than LSTM.
