Abstract
With the rapid development of mobile internet technology, there are a large number of unstructured data in dynamic data, such as text data, multimedia data, etc., so it is essential to analyze and process these unstructured data to obtain potentially valuable information. This article first starts with the theoretical research of text complexity analysis and analyzes the source of text complexity and its five characteristics of dynamic, complexity, concealment, sentiment, and ambiguity, combined with the expression of user needs in the network environment. Secondly, based on the specific process of text mining, namely, data collection, data processing, and data visualization, it is proposed to subdivide the user demand analysis into three stages of text complexity acquisition, recognition, and expression, to obtain a text complexity analysis based on text mining technology. After that, based on computational linguistics and mathematical-statistical analysis, combined with machine learning and information retrieval technology, the text in any format is converted into a content format that can be used for machine learning, and patterns or knowledge are derived from this content format. Then, through the comparison and research of text mining technology, combined with the text complexity analysis hierarchical structure model, a quantitative relationship complexity analysis framework based on text mining technology is proposed, which is embodied in the use of web crawler technology. Experimental results show that the collected quantitative relationship information is identified and expressed in order to realize the conversion of quantitative relationship information into product features. The market data and text data can be integrated to help improve the model performance and the use of text data can further improve predictions for accuracy.
Highlights
With the rapid development of mobile internet technology, there are a large number of unstructured data in dynamic data, such as text data, multimedia data, etc., so it is essential to analyze and process these unstructured data to obtain potentially valuable information. is article first starts with the theoretical research of text complexity analysis and analyzes the source of text complexity and its five characteristics of dynamic, complexity, concealment, sentiment, and ambiguity, combined with the expression of user needs in the network environment
After that, based on computational linguistics and mathematical-statistical analysis, combined with machine learning and information retrieval technology, the text in any format is converted into a content format that can be used for machine learning, and patterns or knowledge are derived from this content format. en, through the comparison and research of text mining technology, combined with the text complexity analysis hierarchical structure model, a quantitative relationship complexity analysis framework based on text mining technology is proposed, which is embodied in the use of web crawler technology
This paper measures the difficulty of words based on domain knowledge, and transforms the word embedding model with text information, so that the word embedding can contain readable information [24]. e innovative points of this paper are as follows: (1) Combining mathematics, information science, and linguistics knowledge, using corresponding computer software, this paper studies the interaction between the modal verb meaning and context characteristics, and excavates hidden knowledge in the language data structure; (2) mining the relationship between the semantic conceptual structure data using the unique attribute feature extraction method based on formal concept analysis
Summary
For modal verbs such as semantic complex are highly sensitive to the context in terms of parts of speech, only considering the co-occurrence of semantic and syntactic characteristics has great limitations, difficult to fully disclose and found that the nature of the interaction between the modal, semantic, and contextual relationship, there is a need to consider the multidimensional context characteristics, more comprehensive, highlights the semantic disambiguation considering different characteristics of the importance of context in [7]. E innovative points of this paper are as follows: (1) Combining mathematics, information science, and linguistics knowledge, using corresponding computer software, this paper studies the interaction between the modal verb meaning and context characteristics, and excavates hidden knowledge in the language data structure; (2) mining the relationship between the semantic conceptual structure data using the unique attribute feature extraction method based on formal concept analysis. Taking the English modal verb MUST as the target word, this paper constructs the formal background of the relationship between the different meaning objects of MUSTand the co-occurrence context features From based on this background, the use of unique properties, characteristics calculation method to obtain MUST, present different meanings of simple unique attributes, such unique characteristics, and unique compound attribute characteristics, these characteristics as the meaning of classification rules, through the comparative analysis of the meaning of classification rules, found that modal verb MUST word meaning and the interaction between different contextual characteristics. 1.5 210.8 210.9 211 211.1 211.2 211.3 211.4 211.5 211.6 211.7 Lexical relational category
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