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CLASSIFYING UNSTRUCTURED COMPUTER TEXT FOR COMPLAINT-SPECIFIC INTERACTIONS USING RULES-BASED AND MACHINE LEARNING MODELING
CLASSIFYING UNSTRUCTURED COMPUTER TEXT FOR COMPLAINT-SPECIFIC INTERACTIONS USING RULES-BASED AND MACHINE LEARNING MODELING
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机译：使用基于规则和机器学习模型为投诉特定的交互分类非结构化计算机文本
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摘要
Methods and apparatuses are described for analyzing unstructured computer text for identification and classification of complaint-specific interactions. A computer data stores unstructured text. A server computing device splits the unstructured text into phrases of words. The server generates a set of tokens from each phrase and removes tokens that are stopwords. The server generates a normalized sentiment score for each set of tokens. The server uses a rules-based classification engine to generate a rules-based complaint score for each set of tokens. The server uses an artificial intelligence machine learning model to generate a model-based complaint score for each set of tokens. The server determines determine whether each set of tokens corresponds to a complaint-specific interaction based upon the rules-based complaint score and the model-based complaint score.
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