# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 4 Results
### Results  
Accuracy, Precision, Recall, and \(F1\) of the proposed model are calculated in the experiment, and the Transformer model, LSTM model, and Bi-LSTM model are used as the contrast experiments. The results of the experiment on the public dataset are shown in Table 4.  
The results of the experiment on the private dataset are shown in Table 5.  
The _Accuracy_ of the LSTM model is 90.21% and 94.82%, on the public and private datasets respectively, which shows that the LSTM network can extract sequence features in sentences and has some effect when applied to the classification of agricultural product reviews. The _Accuracy_ of the Bi-LSTM model is 90.99% and 95.10%, indicating that the classification efficiency and performance of the Bi-LSTM model are better than the single LSTM model on sequential text. The _Accuracy_ of the Transformer model is 91.20% and 95.03%, indicating that the model based on multi-head attention will achieve better sentiment analysis than the model based on historical information of the sequence.  
On the public dataset, the AgriMFLN model proposed in this paper is superior to other methods, with an _Accuracy_ of 92.92%, which is 2.71, 1.93, and 1.72 percentage points higher than the LSTM model, Bi-LSTM model, and Transformer model, respectively, which achieved good results. The _Recall_ is 93.54%, which is 2.71, 4.06, and 8.96 percentage points higher than the LSTM model, Bi-LSTM model, and the Transformer model, respectively, while its _Precision_ is slightly lower than the other models. \(F1\)-_score_, as a comprehensive indicator, balances the impact of _Precision_ and _Recall_ and comprehensively evaluates the advantages and disadvantages of a classifier. The \(F1\)-_score_ of the model proposed in this experiment is 92.97%, which is 2.70, 2.12, and 2.40 percentage points higher than the LSTM model, Bi-LSTM model, and Transformer model, respectively.  
On the private dataset, the proposed model achieved an _Accuracy_ of 96.06%, which is 1.24, 0.96, and 1.03 percentage points higher than the LSTM, Bi-LSTM, and Transformer models, respectively. The _Precision_ is 96.41%, which is 1.45 and 2.04 percentage points higher than the LSTM and Transformer models, respectively. The _Recall_ is 98.10%, which is 2.50 percentage points higher than the Bi-LSTM model. The \(F1\)-_score_ is 97.25%, which is 0.84, 0.73, and 0.67 percentage points higher than the three baseline models, respectively.  
Overall, the results of the experiment show that the AgriMFLN model proposed in this paper performs better than the other models on both public and private datasets. All evaluation metrics of the model proposed are generally higher than the LSTM model, Bi-LSTM model, and Transformer model, except for the _Precision_, which shows that the method proposed in this experiment is superior to other models in agricultural product datasets and has good performance to agricultural product texts. These results demonstrate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline LSTM & 90.21\% & 89.71\% & 90.83\% & 90.27\% \\ Bi-LSTM & 90.99\% & 92.27\% & 89.48\% & 90.85\% \\ Transformer & 91.20\% & 97.48\% & 84.58\% & 90.57\% \\ AgriMFLN & 92.92\% & 92.49\% & 93.54\% & 92.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the experiment on the public dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline LSTM & 94.82\% & 94.96\% & 97.90\% & 96.41\% \\ Bi-LSTM & 95.10\% & 97.45\% & 95.60\% & 96.52\% \\ Transformer & 95.03\% & 94.37\% & 98.90\% & 96.58\% \\ AgriMFLN & 96.06\% & 96.41\% & 98.10\% & 97.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the experiment on the private dataset.  
the effectiveness of the AgriMFLN model in accurately classifying agricultural data and its potential for practical applications in the field.