# 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. sentiment analysis; agricultural product reviews; machine learning; LSTM; attention +
Footnote †: journal: Medical  
Sentiment analysis for agricultural product reviews has important practical significance and plays a crucial role in the production, processing, sales, marketing, and consumer decision-making. They can help to explore consumers' attitudes and emotions toward agricultural products, optimize the quality and taste of agricultural products, improve the efficiency of agricultural product sales and circulation, and enhance the brand image and reputation of agricultural products. Given the natural, diverse, and seasonal characteristics of agricultural products, and their regional and dispersed production, the review information on agricultural products has strong contextual relevance and complex language, making it difficult to assess sentiment tendencies. Due to the diversity of products and the complex language used in reviews, conducting sentiment analysis of agricultural product reviews is a complex task. The following difficulties need to be overcome in order to analyze the sentiment content of agricultural product reviews successfully:  
1. Agricultural product reviews vary in length and often contain information regarding the origin, variety, and nutritional value of the agricultural products. This context is closely related to the sentiment tendency, making it a challenging task. 2. Agricultural product reviews often use more complex language to express emotions, such as the use of modifiers and metaphors. These language structures can be difficult to handle during the process of sentiment analysis. To deal with the above challenges, we have proposed Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews (AgriMFLN). 1. For challenge one, we designed the LSTM layer to capture the sequence features of sentences and then conducted sentiment analysis. By enabling the model to capture the sequential nature of language and extract the emotional content from the input, the LSTM layer played a critical role in sentiment analysis of agricultural product reviews. 2. For challenge two, multi-head attention strategies were implemented to extract the semantic content of phrases. Complex semantic information can be extracted by fusing the vectors produced by the multi-head attention with the vectors produced by the embedding layer. In conclusion, the contributions of this paper can be summarized as follows:  
1. We have developed a model named 'AgriMFLN' for sentiment analysis of agricultural product reviews. This integrated deep learning model incorporates multi-level semantic information, timing sequencing information, and contextual information of sentences to provide a comprehensive analysis. 2. The model is capable of handling long series of agricultural product reviews effectively.