# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 6 Conclusions  
In this paper, we have proposed a new model named Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews (AgriMFLN). Considering that agricultural product reviews are characterized by varying lengths, strong contextual relevance, and complex semantic information, current methods face challenges in effectively and accurately extracting the rich semantic information to identify the sentiment tendencies of agricultural product reviews. To address these shortcomings, we have proposed a method based on the improved LSTM model with the attention strategy. We argue that the attention mechanism can identify the target area that requires more attention and can allocate additional resources accordingly, which is suitable of this scenario of sentiment analysis of agricultural product reviews. This enables the model to focus on the relevant information while ignoring irrelevant details, thereby gathering more detailed and meaningful information about the target. In this study, we implement the multi-head attention mechanism to extract multi-level semantic information in sentences. The resulting semantic features are then fused with the Embedding layer. The LSTM network is trained using these inputs to classify product reviews, which is effective in dealing with the long sequence information and contextual relationships in agricultural product review texts.  
The proposed method has shown superior performance when compared to existing benchmark models and shows that it is effective for sentiment analysis of agricultural product reviews. Conducting sentiment analysis on agricultural product reviews can help consumers accurately understand the quality and taste of agricultural products. At the same time, it can also help production enterprises better grasp market demand, adjust product quality, and taste characteristics, and improve the market competitiveness of their products, so that they can enhance their brand image and reputation of agricultural products. Therefore, sentiment analysis of agricultural product reviews has important practical significance and value. The findings of this study have practical implications for e-commerce platforms and agricultural product manufacturers, providing guidance for improving product quality and customer satisfaction.  
Due to time constraints, it should be noted that although the model proposed in this experiment achieved good results on two datasets, these results were only tested on the specific dataset used in this experiment and have not been tested on a more extensive dataset of agricultural product reviews, or even on language data from other fields. In the future, it is necessary to further expand the scale of the experiment and increase testing on different fields and types of corpora, to verify the model's generalization ability, reliability, and practicality. In addition, we have noticed that the efficacy of using prior knowledge has been proven [48], this will also be one of the future research directions of this article.  
Conceptualization, R.L.; methodology, R.L.; software, R.L. and Y.L.; validation, R.L.; formal analysis, R.L.; investigation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, R.L. and Y.L.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 32071775) and the Development Research Centre of Beijing New Modern Industrial Area (grant number JD2021001).  
Not applicable.  
The data used to support the findings of this study are available from GitHub.  
The authors declare no conflict of interest.