Sentiment analysis of commodity reviews based on ALBERT-LSTM

Abstract

Sentiment analysis of product reviews has now become one of the important research directions in the field of NLP, which is of great significance in helping merchants better understand user preferences and providing decision-making basis for other users to purchase products. Existing product review sentiment analysis models based on deep learning mostly use traditional word vector models. It is difficult to obtain the contextual semantic information of words, and there is a problem of "polysemous one word". A product review sentiment analysis model combining ALBERT and LSTM is proposed. First, we use the ALBERT pre-training model to get the word vector containing sequence and semantic information. Then we use the LSTM model that can obtain long-distance semantic features for training. Finally, the emotional polarity of product reviews is classified and output through the emotional polarity discrimination layer. The experimental results on the data set of digital products of JD Mall show that the F1 value of the ALBERT-LSTM model is improved compared with the ALBERT and LSTM models.