AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

Abstract

With the rapid development of the Internet, agricultural products have entered e-commerce platforms, and agricultural product reviews have become an important reference for consumers when purchasing agricultural products. However, due to the characteristics of different lengths, rich context-sensitive information, and multi-level information in the sentences of agricultural product reviews, the existing sentiment analysis methods cannot perform well enough to identify the sentiment tendency. To address this issue, we abstract the problem as a binary classification task to extract consumers’ sentiment orientation by proposing a new method. This method utilizes an attention mechanism to assign different weights to different key information in the sentence, thereby extracting abundant semantic information from the sentence. The design of the long short-term memory (LSTM) gate can effectively solve the problem of extracting long sequences and context-related information. The proposed model achieves superior results on two agricultural product datasets compared to other baseline models, providing guidance for merchants to improve agricultural product quality and enhance customer satisfaction.