RALT—A film review sentiment classification model

With the development of the internet and film industry, filmgoers have published a large amount of review texts in many areas such as film analysis, film sharing, and social media. The interpretation of review texts is diverse and strongly environmentally dependent, which adds more difficulty to the task of text classification. How to quickly and accurately judge the sentiment tendency of review texts and classify them accurately, and improve the application value of review text data has become one of the popular research directions in natural language processing (NLP). This article proposes a film review sentiment analysis model RALT, which combines a text convolutional neural network (TextCNN) with a self-attention mechanism and a fused long short-term memory (LSTM). The model uses the RoBERTa Employing a pre-trained model to produce dynamic textual word embeddings addresses the intricacies associated with polysemy. Sentiment classification experiments were conducted on the IMDB dataset. The experiments were conducted by comparing BERT and RoBERTa with TextCNN, RNN, LSTM, BI-LSTM and other models, and it was found that the proposed model achieved the best accuracy and F1-score in the classification task.The experiment showed that the model is suitable for the field of film review sentiment classification.