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Author information Article notes Copyright and License information Disclaimer 1 Analysis of the Status of Natural Language Processing Technology Based of Deep Learning The Korea Journal of BigData, vol 6 no 1, 2021, pp.63–81 AI and classification of NLP technologies Focuses on an artificial intelligence (AI) classification system and discusses the status of patent-based technology development X This is a review article that organizes theoretical content and does not provide new technology or research Summary of artificial intelligence and natural language processing technologies developed and advanced thus far Considers using BERT and RNNs among natural language processing techniques Opinion data is extracted from text data that mentions the user's opinion about the object itself or some features of the object in a sentence, such as "This software updates really fast." In the case of Korean, this method uses "morphology analysis" and "phrase analysis" as a pre-processing step. Opinion text extracts information, such as the opinion expression, object, and modifier, after proper natural language processing, such as morpheme analysis and parsing, and then determines whether the meaning of a specific word is positive/negative, and strengthens or weakens the meaning to obtain a final polarity value. OLSAP sees the polarity information of the opinion data as an element corresponding to a "measurement", such as sales volume, uses polarity information in the opinion data, and obtains complex information, such as users' overall evaluation of products, regional evaluations, and changes in opinions over time (Myung et al., 2008). The Support Vector Machine (SVM) is a supervised learning model that is mainly used for classification and regression analysis. The SVM uses support vectors to define decision boundaries and to classify unclassified points against those decision boundaries. The study was conducted in the following figure of a flowchart. Open in a separate window Author contributions Go to: designer bags cyber monday
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