## 4 Experimental Design
### Result Analysis
#### 4.3.2 Comparison between the Proposed Method and the Classical Method  
In order to further verify the recognition effectiveness of the proposed method, it is compared with the traditional classifier model Naive Bayes and Support Vector Machine (SVM). The feature representation of TF-IDF method, the optimal feature combination (N\({}_{\text{B}}\), N\({}_{\text{G}}\), N\({}_{\text{PSP}}\), D\({}_{\text{EA}}\), N\({}_{\text{PP}}\), N\({}_{\text{CV}}\), D\({}_{\text{FC}}\), and D\({}_{\text{P}}\)) and the all feature combination (N\({}_{\text{B}}\), N\({}_{\text{G}}\), N\({}_{\text{PSP}}\), D\({}_{\text{FA}}\), N\({}_{\text{PP}}\), N\({}_{\text{CV}}\), D\({}_{\text{P}}\), and N\({}_{\text{IAr}}\)) based on Grounded theory considering purchase decision-making process are all considered as the feature representation to train SVM classifier model, Naive Bayes classifier model and MLP neural network classifier, respectively. The specific experimental results are shown in Figure 6.  
As shown in Figure 6:  
1. In the selection of feature extraction methods, the overall recognition effect of the three classifiers trained by the TF-IDF method is poor, and they only perform well in the recall rate, while the _Precision_uf_, F\({}_{1}\) value and _Precision_ their scores are low compared with those trained by the other two feature representation methods considering consumers' purchase decisions. Since the TF-IDF feature representation method only starts from the perspective of text word frequency and the usefulness of online reviews in real situations is affected by many factors, it can be seen that the feature extraction method carried out in this paper from each stage of consumers' purchase decisions has certain effectiveness;
2. In the comparison of the three feature extraction methods, the optimal feature combination has the best performance, and its recognition effect on the three different classifier models is excellent. For the SVM classifier model, the F\({}_{1}\) value based on optimal feature combinations is between 4.3% and 15.4% higher than those based on all feature combinations and the TF-IDF method. For the Naive Bayes classifier  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Combination** & **Precision** & **Recall** & **F\({}_{1}\)** & **Accuracy** \\ \hline N\({}_{\text{B}}\) & 0.913 & 0.764 & 0.832 & 0.843 \\ N\({}_{\text{B}}\), N\({}_{\text{G}}\) & 0.91 & 0.768 & 0.833 & 0.85 \\ N\({}_{\text{B}}\), N\({}_{\text{G}  
model, the F\({}_{1}\) value based on optimal feature combination is between 1% and 5.4% higher than those based on all feature combinations and the TF-IDF method. For the MLP neural network classifier model, the F\({}_{1}\) value based on the optimal feature combination is 0.4% and 30.1% higher than those based on all feature combinations and the TF-IDF method. It can be concluded that the selection of the optimal feature combination significantly improves the recognition effect of the classifier. Figure 6: Comparison of the recognition effect under different feature representations of three classifiers. The recognition effect of the three classifier models under the optimal feature representation will be further analyzed below. As can be seen from Figure 7, in terms of the selection of classifier models, the MLP classifier has the best overall recognition effect, and all indexes are higher than the other two traditional classifiers. The F1 value of the SVM classification model based on the optimal feature combination is 83.3%, while the F1 value of the MLP classifier is 6% higher than that of the SVM classifier, reaching 89.3%. Because the SVM classifier is also very suitable for the analysis of short text data, the classification effect is not as outstanding as that of the MLP classification model, but it still meets the basic standard. The F1 value of the Naive Bayes classification model based on the optimal feature combination is only 70%, and the F1 value of the MLP classifier is 19.3% higher than that of the Naive Bayes classifier.