Three methods of text analysis

Three methods of text analysis are: word frequency statistics, sentiment analysis and theme modeling.

1, statistical analysis of word frequency

Word frequency statistics is one of the most basic methods in text analysis. It reveals the characteristics and important information of the text by calculating the frequency of each word in the text. By counting the frequency of each word, we can know which words are used frequently in the article, and then infer the theme or key content of the article.

2. Analysis of emotional analysis

Emotion analysis is a method to identify and extract the emotions contained in the text through natural language processing technology. It can analyze the emotional attitudes in the text, such as positive, negative or neutral. Emotional analysis can help us understand the emotional tendency of the text author, the attitude of users' feedback and the trend of market public opinion.

3. Theme modeling and analysis

Topic modeling is a method to automatically discover topics and topics hidden in text through the analysis and mining of large-scale text data. It can classify each document in the text collection into one or more topics and extract the keywords of each topic. The commonly used algorithms for topic modeling include latent Dirichlet distribution (LDA) and implicit Dirichlet distribution (LDA).

Text analysis method and its application

1, the application of text classification

Text classification is a method of automatically classifying texts according to predefined categories or labels. It can be used in spam filtering, news classification, sentiment analysis and other fields. Through text classification, we can quickly and accurately extract the required information from a large number of text data and provide personalized recommendations and services for users.

2. The application of text clustering

Text clustering is a method to gather similar texts together. It can automatically group texts according to their contents and characteristics, thus revealing the potential patterns and structures in text data. Text clustering can be applied to news aggregation, user portrait analysis and other fields to provide users with more accurate information push and personalized services.

3. Application of relationship extraction

Relationship extraction is a method to extract relationships between entities from texts. It can automatically identify and extract the entities in the text and their relationships, such as the relationship between products and prices, the relationship between people and so on. Relationship extraction can be applied to knowledge map construction, question answering system and other fields to provide users with more accurate information query and knowledge acquisition channels.