Takeaway platform reviews are users’ direct feedback on the platform service quality, mining and analyzing the emotional connotation of review data has important significance for the improvement of takeaway platform service quality. This paper adopts Meituan catering takeaway platform as the data source for the study, and utilizes text de-emphasis and filtering phrase methods for data preprocessing of the comment corpus text. Then a bidirectional LSTM takeout review data sentiment classification network is proposed to construct a bidirectional LSTM sentiment classification model. On the basis of this model, a self-attention mechanism is added to fully mine and learn the relevant laws of the takeout platform comment data to improve the accuracy and reliability of the model’s takeout comment sentiment classification results. Incorporating the sample screening strategy of uncertainty and diversity to enrich the sample variety and improve the accuracy of the model’s classification and prediction of the sentiment of the sample corpus text in the training set, the sentiment classification model based on the self-attention mechanism is constructed. Compared with similar model algorithms, the accuracy and precision of the model in predicting and categorizing the sentiment of the review corpus text of takeaway platforms is as high as 93.10% and 94.51%, which is suitable for the needs of the sentiment processing of the review data corpus text of takeaway platforms.
