Albert-based sentiment analysis of movie review

摘要

Movie reviews include the real evaluation of the movie by the public. Through these reviews, the audience can better judge whether the movie is worth watching. However, as the amount of data on movie reviews continues to grow, it takes a lot of manpower and material resources to manually analyze the emotional tendency of each movie review. As an important research field of machine learning, sentiment analysis focuses on extracting topic information from text reviews. The field of sentiment analysis is closely related to natural language processing and text mining. It can be successfully used to determine the reviewer's attitude towards various topics or the overall polarity of the review. As far as movie reviews are concerned, in addition to scoring movies digitally, they can also quantitatively enlighten us on the advantages and disadvantages of watching movies. This article uses the Albert model to build a classifier, and uses the "movie review dataset" issued by Stanford University for network training. Experiments show that the trained Albert model can reach an accuracy of 89.05% when performing sentiment analysis of movie reviews. Compared with the traditional LSTM and GRU, the accuracy of the Albert model is improved by 3%.