## 3 Results
### Experimental Results of FK-SVM, HIST-SVM and PLSA-SVM  
To verify the superiority of the FK-SVM method, a comparison between FK-SVM method and text sentiment analysis method is used in this section based on HIST-SVM and PLSA-SVM, to prove [6]. The comparison includes accuracy and recall rate. The histogram of words appearing in the document is mainly used as the feature of the document in HIST-SVM method, and the histogram feature is submitted to the SVM for text sentiment analysis. The PLSA-SVM method mainly consists of two parts. First, the PLSA algorithm is used to simulate the probability distribution of text and text topic features are used to identify text. Secondly, the text theme features transferred to support vector machine are applied as features of sentiment analysis for classification, that is, text sentiment analysis. Combining the advantages of PLSA generative model and SVM discriminative model, this method is called as PLSA-SVM method which uses topics as hidden variables and uses EM algorithms to estimate model parameters. Detailed training methods can be obtained in reference [6]. The comparison method involves multiple rounds of experimental comparisons and comparisons with different percentages of samples. Two experiments are conducted in this paper:  
1. On the twitter data set, five rounds of cross-validation training and testing are carried out for FK-SVM method, HIST-SVM method and PLSA-SVM method, followed by the obtaining and comparison of the accuracy, recall rate and corresponding average value of each round. 2. On the twitter data set, FK-SVM, HIST-SVM method and PLSA-SVM methods are trained and tested in 5 rounds with different proportions of training samples, followed by the obtaining and comparison of the accuracy, recall rate and corresponding average value of each round. Main experimental results and analysis:  
Experiment 1: In the first experiment, FK-SVM method, HIST-SVM method and PLSA-SVM method are compared for several rounds of experiments, followed by the use of the recognition accuracy and recall rate as evaluation criteria to verify the effectiveness of HIST-SVM and PLSA-SVM algorithm. In this section, the FK-SVM method, as long as tested, can be compared with the HIST-SVM and the PLSA-SVM experimental results. Table 1 and Figure 2 present the experimental results of the algorithm, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Round** & **HIST-SVM** & **PLSA-SVM** & **FK-SVM** \\ \hline Round 1 & 83.12\% & 84.42\% & 88.07\% \\ Round 2 & 81.53\% & 83.71\% & 85.13\% \\ Round 3 & 83.51\% & 82.16\% & 86.93\% \\ Round 4 & 81.89\% & 83.07\% & 87.35\% \\ Round 5 & 82.37\% & 82.64\% & 88.52\% \\ Average & 82.49\% & 83.20\% & 87.20\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline
**(a)** & & & \\ \hline Round 1 & 84.45\% & 86.01\% & 89.00\% \\ Round 2 & 83.31\% & 85.03\% & 87.41\% \\ Round 3 & 81.22\% & 85.94\% & 89.05\% \\ Round 4 & 84.58\% & 87.05\% & 88.19\% \\ Round 5 & 82.63\% & 84.31\% & 87.83\% \\ Average & 83.24\% & 85.67\% & 88.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The multi round compared experimental results of FK-SVM, HIST-SVM and PLSA-SVM. (**a**) Precision. (**b**) Recall rate. Experiment 2: The second experiment was carried out on the basis of the first experiment. Table 2 compares the results of experiments using the FK-SVM algorithm proposed in this paper with those using the HIST-SVM and the PLSA-SVM algorithm.