Text emotion classification system based on multifractal methods

Emotion classification is an attractive research, emotion classification based on machine learning has broad application prospects in the field of computer. It is particularly important to construct an effective and accurate algorithm to extract emotional features from subjective texts, and classify them into corresponding emotional categories. In this paper, we implement a novel classification system for identifying emotions in a positive and negative corpus dataset. The corpus contains hotel reviews of Chinese emotion mining, and we choose 1000 positive and 1000 negative corpus as the dataset for analysis. We first preprocess the positive and negative texts, and after obtaining the characteristic word text of positive and negative corpus, we use Word2Vec model to transform the sentence into a numerical vector. We regard the obtained feature word vector as a time series, and adopt three multifractal analysis on the time series, such as multifractal detrended fluctuation analysis (MF-DFA), Multifractal detrended moving average (MF-DMA), and Multifractal detrended weighted average algorithm of historical volatility (MF-DHV). After extracting the Hurst exponents of the vector time series, we put the features as the input vector into SVM for classification analysis. We utilize four key values such as Accuracy (Acc), Precision (P), Recall (R) and F-score (F1) to evaluate the classification experiments. The results suggest that Word2Vec-MF-DHV-SVM model is more effective and competitive in extracting information from corpus numerical vector.