Text sentiment analysis methods (2) - Traditional machine learning-based sentiment analysis methods

Introduction of traditional machine learning-based sentiment analysis methods

Machine learning is a learning method that trains a model from given data and predicts the results by the model. This method has been studied so far and has achieved many effective results.

Machine learning based sentiment analysis method refers to the extraction of features through a large amount of labeled or unlabeled corpus, using statistical machine learning algorithms, and finally outputting results in sentiment analysis.

Machine learning based sentiment classification methods are divided into three main categories: supervised, semi-supervised and unsupervised methods.

In the supervised methods, different sentiment categories can be classified by giving a sample set with emotional polarity. The supervised methods are more dependent on data samples and spend more time on manual labeling and processing of data samples. The common supervised methods are KNN, Naive Bayes and SVM.

In semi-supervised methods, the text sentiment classification results can be effectively improved by feature extraction from unlabeled text, and this method can effectively solve the problem of sparse data sets with labeling.

In unsupervised methods, unlabeled text is classified based on the similarity between texts, and this method is less used in sentiment analysis.

Advantages and disadvantages:

Traditional machine learning-based sentiment classification methods mainly focus on the extraction of sentiment features and the combination of classifiers, and the combination of different classifiers has a certain impact on the results of sentiment analysis. These methods often cannot make full use of the contextual information of the text, and have the problem of ignoring the contextual semantics when analyzing the text content, so their classification accuracy is affected.