Abstract
With the rapid growth of user-generated content, unsupervised methods that do not require label training data have gradually become a research focus in the field of sentiment classification and natural language processing. But the performance of unsupervised methods is unsatisfactory. This is because the ambiguity of sentiment polarity and the fuzziness of sentiment intensity are usually ignored in existing unsupervised methods. To address these problems, we propose an unsupervised sentiment classification method based on multi-level fuzzy computing and multi-criteria fusion which involves three innovations. Firstly, we come up with a multi-level computing model to compute the sentiment intensity of reviews for partly reducing the ambiguity of sentiment polarity. Secondly, to further decrease the ambiguity of sentiment polarity, a multi-criteria fusion strategy based on sentiment category credibility and domain category representativeness is proposed. Thirdly, a fuzzy classifier is constructed to solve the fuzziness of sentiment intensity. Furthermore, a self-supervised method using pseudo-labeled training data is proposed to learn the optimum parameters of the fuzzy classifier. Experimental results in three different domain balanced datasets and one unbalanced dataset proved that our method improves 12.35% more accuracy than the competitive baselines in sentiment classification.
Highlights
With the rapid development of e-commerce and social media, online reviews are growing explosively
The sentiment classification method based on classical machine learning usually uses the bag of words (BOW) model to represent text and uses Naive Bayesian (NB), Expectation-Maximization algorithm (EM), Random Forest and support vector machine (SVM) as classifiers [8]–[11]
In order to solve the three main problems in the existing unsupervised sentiment classification methods, we propose an unsupervised sentiment classification method based on multi-level fuzzy computing and multi-criteria fusion
Summary
With the rapid development of e-commerce and social media, online reviews are growing explosively. There are two kinds of supervised sentiment classification methods, one is based on machine learning, the. In the supervised sentiment classification methods, the text representation model and classification method are two very important parts. Different supervised sentiment classification methods differ only in text representation models and classification methods. The sentiment classification method based on classical machine learning usually uses the bag of words (BOW) model to represent text and uses Naive Bayesian (NB), Expectation-Maximization algorithm (EM), Random Forest and support vector machine (SVM) as classifiers [8]–[11]. The sentiment classification method based on deep learning generally use end-to-end pattern and implement text representation and classifier based on the neural network [12]–[16], [37], [38].
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