基于双层树状支持向量机的观点挖掘与倾向分析

View mining and trend analysis based on double-layer tree Support Vector Machine

本文通过进行大量预处理工作,将经过词袋模型和Word2Vec两种不同向量化方法处理后的文本数据分别输入到SVM和LSTM模型中,训练出可以识别文本情感倾向的模型。进而对新产生的评论进行分类。根据实际数据量的倾斜状况,基于传统机器学习算法支持向量机(SVM),本文提出双层支持向量机,采用2种不同的方法分别训练模型并预测。最后再使用深度学习算法长短时记忆模型(LSTM)再次训练并预测,并对这3种方法做出比较和总结。结果显示,双层SVM比单层SVM的准确度提高了8个百分点;而LSTM比单层SVM低了2个百分点,比双层SVM低了接近10个百分点。

In this paper,a large amount of preprocessing work is carried out,and the text data processed by the following two different vectorization methods as the word bag model and Word2 Vec are input into the SVM and LSTM models,respectively to train a model that can recognize the emotional tendency of the text.Further the newly generated comments are classified.According to the tilt of the actual data volume,based on support vector machine(SVM)that is the traditional machine learning algorithm,this paper proposes a two-layer support vector machine,using two different methods to train the model and predict.Thus,the deep learning algorithm long-term memory model(LSTM)is used to train and predict again,and the three methods are compared and summarized.The results show that the accuracy of the two-layer SVM is 8 percentage points higher than that of the single-layer SVM;while the LSTM is two percentage points lower than the single-layer SVM,which is nearly 10 percentage points lower than the double-layer SVM.