The lack of effective evaluation of online education is a worldwide malpractice, and it is impossible to help students improve the correctness of online learning choices through existing reviews. Based on the current mainstream sentiment lexicon and text sentiment analysis, the authors use machine learning method to analyze the sentiment orientation of the legal course review text, through method that combines PMI and SVM. At the same time, this paper uses LibSVM tool to train and predict data, collect and pre-process data through network data collection, and, based on traditional algorithms, propose improved experimental scheme based on their respective advantages and disadvantages. In addition, the model proposed in this study is used to classify and process the emotional text, and the two methods are combined to obtain the final result. Finally, this paper combines experiments to analyze the performance of the comprehensive model proposed in this study. The research shows that the classification effect of the text sentiment analysis of model is good, it can be applied to practice, and it can provide theoretical reference for subsequent related research.
ShiJ., LeeW.J., LiuY.O., et al. Forecasting power output of photovoltaic system based on weather classification and support vector machine, IEEE Transactions on Industry Applications48(3) (2015), 1064–1069.
2.
LiuD., NiuD., WangH., et al. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm, Renewable Energy62 (2014), 592–597.
3.
BuiD.T., TuanT.A., KlempeH., et al. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree, Landslides13(2) (2016), 361–378.
4.
TianY., QiZ., JuX., et al. Nonparallel Support Vector Machines for Pattern Classification, IEEE Transactions on Cybernetics44(7) (2013), 1067.
5.
PengL., NiuR., HuangB., et al. Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China Geomorphology204 (2014), 287–301.
6.
KavzogluT., SahinE.K., ColkesenI.. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression, Landslides11(3) (2014), 425–439.
7.
KremerJ., Steenstrup PedersenK., IgelC.. Active learning with support vector machines, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery4(4) (2014), 313–326.
8.
AzarA.T., El-Said.S.A.. Performance analysis of support vector machines classifiers in breast cancer mammography recognition, Neural Computing & Applications24(5) (2014), 1177.
9.
Márciodas Chagas Moura, ZioE., LinsI.D., et al. Failure and reliability prediction by support vector machines regression of time series data, Reliability Engineering & System Safety96(11) (2017), 1527–1534.
10.
HuS., YaoY.D., Yang.Z.. MAC protocol identification using support vector machines for cognitive radio networks, IEEE Wireless Communications21(1) (2014), 52–60.
11.
KangM., KimJ., KimJ.M., et al. Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis, IEEE Transactions on Power Electronics30(5) (2015), 2786–2797.
12.
AtienzaF.A., MorgadoE., FernandezmartinezL., et al. Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines, IEEE Trans Biomed Eng61(3) (2014), 832–840.
13.
JegadeeshwaranR., SugumaranV.. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines, Mechanical Systems and Signal Processing52–53 (2015), 436–446.
14.
De YongD.. BhowmikS. and MagnagoF., An effective Power Quality classifier using Wavelet Transform and Support Vector Machines, Expert Systems with Applications42(15–16) (2015), 6075–6081.
15.
HalderS., BenschM., MellingerJ., et al. Online artifact removal for brain-computer interfaces using support vector machines and blind source separation, (Research Article) Computational Intelligence & Neuroscience, 2007(10) (2014), 8.
16.
ClaesenM., De SmetF., SuykensJ.A.K., et al. EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines, Journal of Machine Learning Research15(1) (2014), 141–145.
17.
GaoL., LiJ., KhodadadzadehM., et al. Subspace-Based Support Vector Machines for Hyperspectral Image Classification, IEEE Geoscience and Remote Sensing Letters12(2) (2015), 349–353.
18.
IfenthalerD., WidanapathiranaC.. Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines Technology, Knowledge and Learning19(1-2) (2014), 221–240.
19.
RienowA., GoetzkeR.. Supporting SLEUTH – Enhancing a cellular automaton with support vector machines for urban growth modeling Computers, Environment and Urban Systems49 (2015), 66–81.
20.
CaoR., WangZ., WangY., et al. SMOQ: A tool for predicting the absolute residue-specific quality of a single protein model with support vector machines, BMC Bioinformatics15 (2014).
21.
DingS., HuaX.. Recursive least squares projection twin support vector machines for nonlinear classification, Neurocomputing130(3) (2014), 3–9.
22.
PengJ., ZhouY., ChenC.L.P.. Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification, IEEE Transactions on Geoscience & Remote Sensing53(9) (2015), 4810–4824.
23.
LinT.C., Yu.P.T.. Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration, Neural Computation16(2) (2004), 333–354.
24.
KerchevalA.N., ZhangY.. Modelling high-frequency limit order book dynamics with support vector machines, Quantitative Finance15(8) (2015), 1–15.
25.
MajidA., AliS., IqbalM., et al. Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines, Computer Methods & Programs in Biomedicine113(3) (2014), 792–808.
