Author: Zen and the Art of Computer Programming
1.1 Overview
Text classification is an important task of NLP (Natural Language Processing, Natural Language Processing). Text classification is to divide a piece of text into a certain category or multiple categories. For example, spam filtering, news classification, community Q&A, personalized recommendation, etc. Sentiment Analysis is also an important task in NLP. It can analyze the emotional polarity (positive or negative) expressed by a given text. For example, the emotional polarity of product reviews, whether forum posts are positive or negative, and the attitude changes of users on platforms such as Weibo, News, Facebook, etc. In practical application scenarios, automatic text classification and sentiment analysis are sometimes required. However, traditional classification methods often cannot accurately identify the emotional tendency of texts. Therefore, how to improve the effect of text classification and sentiment analysis has become one of the concerns of researchers. This article will introduce a text classification and sentiment analysis method based on Bayesian classifiers. The method is realized by word frequency statistics, multinomial Bayesian method and naive Bayesian method. At the same time, compare this method with other classification methods, and explain its advantages and disadvantages. Finally, the limitations and future directions of the method in real-world applications are discussed.
1.2 Related work
(1) Overview of classification methods
Over the past few decades, many different text classification methods have been proposed. The most famous of these is the Naive Bayes method (Naive Bayes, NB), which assumes that all features are independent of each other, and each feature has the same conditional probability distribution. There are also some improved versions of Naive Bayesian methods such as Gaussian Naive Bayesian (GNB) and Weighted Least Squares (WLS). In general, the common feature of these methods is that they are able to classify documents. but