## 3 Results
### Experiment Analysis of Experiment 2  
Test experiments are conducted using the method proposed in the previous section, with the classification performance being presented in Table 2. The average accuracy and recall rate of FK-SVM are 80.96% and 79.68%, respectively. 1. Comparison between FK-SVM and HIST-SVM:  
Table 2 and Figure 3 indicate that the HIST-SVM method is basically stable when faced with different percentages of training samples, while the test results of FK-SVM method slightly improve with the increase of the percentage of training samples This is because when the training samples are few, the probability distribution of vocabulary, documents and emotional topics in the samples will be greatly affected by noise. As the proportion of the sample increases, the corresponding probability distribution will gradually approach to the real distribution, thus making the topic mining more accurate, and improving the effect on the test set. But overall, the average accuracy and recall rate of the FK-SVM method are more obvious than those of the HIST-SVM method. 2. Comparison between FK-SVM and PLSA-SVM:  
Experiment 2 shows that the FK-SVM method is basically stable when faced with different percentages of training samples, and the test results slightly improve with the increase of percentage of training samples. This is can be attributed to the Fisher kernel function in the support vector machine. More specifically, the probability distribution information of the data has been taken into consideration when deriving the Fisher kernel function. In addition, the probability distribution of vocabulary, documents and emotional topics in samples, influenced by the small number of training samples, is greatly affected by noise. As the sample ratio increases, the corresponding probability distribution gradually approaches to the real distribution, thus making the topic mining more accurate, and improving the effect on the test set. But overall, the average accuracy and recall rate when using FK-SVM method are slightly higher than those using PLSA-SVM. This section and the preceding section illustrate the specific experimental method of the FK-SVM method in text sentiment analysis, along with the comparison of it with the PLSA-SVM method. The experimental results demonstrate that accuracy and recall are higher when using the FK-SVMmethod than using the PLSA-SVM method. Theoretically, when deriving the Fisher kernel function based on the PLSA model, the probability distribution information of the data, and the high-level semantic information of the PLSA, especially, the statistical information of the text itself are taken into account. This allows the support vector machine based on Fisher kernel function to take the probabilistic information of text units and the latent emotional topics of text as classification features in text classification, thus improving the effect of classification. The algorithm proposed in this section can better perform sentiment analysis on the text. The classification accuracy is proportional to the number of training samples within a certain range. More specifically, the training complexity and training time of the model will not be affected by the scale of test sets. Furthermore, this method can be applied to large-scale text sentiment classification without additional training process. In the twitter dataset used in this paper, the difference between the HIST-SVM method and the PLSA-SVM method is slightly smaller than that in the dataset composed of Internet posts. This is because there are many sources of Internet posts whose distribution is more representative of the probability distribution of real data, while twitter, as a single data source, has a tendency of cohesion in its topics, making its probability distribution less close to the real distribution than that of Internet posts. Hence, the PLSA-SVM effect is slightly reduced, while HIST-SVM shows little effect.