# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 2 Literature Review
Therefore, it does not need to build a sentiment dictionary and manually extract semantic features, which saves manpower as well as material resources and does not require long-term maintenance. Secondly, the model adopts automatic extraction of sample features, which improves the scalability of the model and has a better effect than the lexicon-based method. Nowadays, researchers are using more diversified and multi-level deep learning algorithms, and they are making the corresponding adjustments to the algorithm details such as the connection method and activation function of neurons. Deep learning first appeared in computer speech recognition and visual recognition and then was used in the field of NLP. It has become a popular technology in NLP and is now widely used. Many scholars have used the study of neural networks to successfully complete the sentiment analysis assignment. [PERSON] et al. [20] figured that classification performance can be improved through deep learning. They found that repeated neural networks and transfer learning are always better than classical machine learning. Additionally, they suggested utilizing transfer learning as an inductive method of transferring knowledge from related NLP tasks. [PERSON] [21] proposed applying convolutional neural networks (CNN) to text sentiment analysis. The model used the Word2 Vec model to convert text into word vector form, and then input the word vector as one-dimensional data into CNN for classification learning using convolution kernels of different volumes. An LSTM network is a unique kind of recurrent neural network (RNN) that is effective in dealing with lengthy sequences of data and discovering long-term dependencies. [PERSON] et al. [22] proposed a CNN-LSTM model applied to personality prediction, which used CNN to extract the spatial features and used the LSTM network for further extraction. The model paid more attention to emotion-related words or their corresponding target information during training. To accelerate the training of the model, six different parameter-reduced slim versions of the LSTM model were suggested by [PERSON] et al. [23]. These variant models can be selected appropriately to reduce computational costs. [PERSON] et al. [24] proposed a multi-dimensional topic classification model based on LSTM. The model constituted hierarchical multi-dimensional sequence computations and was composed of LSTM cell networks. The experimental findings revealed that the average precision of the proposed model ranged from 91% to 96.5%. [PERSON] et al. [25] proposed a target-dependent long short-term memory (TD-LSTM) model, which used two LSTMs to model the semantic relationship between target words and their contextual words. Experimental results showed that incorporating target information into the LSTM can significantly improve classification accuracy and achieve better performance. [PERSON] et al. [26] used the LSTM network to classify COVID-19-related comments. Their findings emphasized the significance of using the general public's opinions and relevant computational tools to comprehend COVID-19-related issues and guide related decision-making. [PERSON] et al. [27] adopted a different approach to processing COVID-19-related comments. They proved the significance of stacked word embeddings in the deep learning model. The findings of the experiment also demonstrated the superior performance of stacked word embeddings in coping with the distinctive contextual semantic understanding from small tweets and the unbalancedness of the experimental dataset. [PERSON] et al. [28] proposed a stacking model named word attention-based Bi-directional long short-term memory (Bi-LSTM) and CNN stacking model to analyze the emotional tendency of fans in regard to films. After the Bi-LSTM layer, the attention layer was included to give text keywords more weight so that the model could extract keywords more effectively. Experiments showed that the classification accuracy and recall were improved. [PERSON] et al.