# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 2 Literature Review
[41] proposed a target-dependent BERT (TD-BERT) model in an aspect-level sentiment classification task, which implemented three target-dependent variants of the BERT model. Compared to traditional feature engineering methods, embedding-based models, and early BERT applications, this model achieved better performance. [PERSON] et al. [42] proposed a broad multitask transformer network (BMT-Net), which took advantage of both feature-based and fine-tuning methods. BMT-Net can learn robust contextual representation and achieve good results. To alleviate the reliance on annotated data, [PERSON] et al. [43] proposed a text sentiment classification model based on the Transformer mechanism that combined knowledge distillation and text augmentation methods. They reduced the number of parameters based on the Transformer mechanism and solved the problem of low accuracy for the few-sample tasks. [PERSON] et al. [44] proposed a multi-head self-attention transformation networks (MSAT) network for aspect-based sentiment tasks. The model used a multi-head target-specific self-attention mechanism to better capture global dependencies and introduced target-sensitive transformation to effectively address the issue of target-sensitive sentiment. Then, part-of-speech features were integrated into MSAT to capture the syntactic features of the sentence. The experiments showed that the proposed model achieved better effectiveness compared with several state-of-the-art methods. In conclusion, scholars in different fields have constructed and improved classification models using both emotion lexicon-based and machine-based methods, to suit various application scenarios. The method based on sentiment lexicons requires the use of pre-constructed lexicons that contain different vocabulary for positive, negative, and neutral sentiment, which can be used to determine the sentiment orientation of the given text. The advantage of this method is that it is easy to understand and interpret, as it directly interprets specific vocabulary in the text. However, the method is limited by the quality of the sentiment lexicon and the application domain, as the lexicon may not include all sentiment words and cannot capture the context of the words, which may lead to errors. In addition, sentiment lexicons need to be updated regularly to reflect new vocabulary and new text data, which requires human and material resources. In contrast, the deep learning-based sentiment analysis method can extract different features in the text, including words, phrases, syntactic structures, context, and context tual information. This method does not require predefined sentiment lexicons, and it can extract sample features and automatically optimize and adjust the model during training, improving the model's scalability and having better performance than the sentiment dictionary-based approach. However, the deep learning-based approach also has drawbacks, such as taking a long time to train, requiring a large amount of training data for learning, and the difficulty of classification tasks when the training dataset is insufficient. This paper draws inspiration from the success of multi-head attention in sentiment analysis, which has been proven effective in extracting information at multiple levels. Specifically, we have proposed a method seeking to improve upon the classical LSTM network by incorporating feature mixing techniques. The goal is to enable the model to capture a broader range of information and improve the accuracy of the evaluation process. By abstracting the agricultural product review evaluation as a sentiment analysis task and leveraging recent advancements in deep learning, the proposed approach represents a promising avenue for improving the efficiency and accuracy of agricultural product review evaluations.