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- W3003641317 abstract "With the growth of the Internet, it is more common for users to spend a lot of time on the Internet to gather information and feedback. On the one hand, sentiment analysis helps clients to fully understand performance. Machine learning blends and sentimental analysis can be useful for product reviews and predicting consumer attitudes towards new products. In this paper to analyze customer behavior of buying a product online using the product review. Multilingual text is published by different categories of users. Intelligent data collection models have been developed using dynamic keywords. Combines a feature set of words from publicly available vocabulary resources in languages, making the proposed emotion extraction framework into a better dataset. Extraction of emotions from feedback is one of the most important tasks in the field of concept mining and sentiment analysis. In this proposed work introduce Multi-Mixed short text ridge analysis (MMSTR) is extract sentence and text extract from customer feedback data. Customer emotion is measured using the online product review database. In this method analysis the psychological factors like human emotion to the text e.g., happy, angry, dislike etc., commands. Results show that efficiency has been calculated in terms of accuracy rate and recall rate several times in other tests." @default.
- W3003641317 created "2020-02-07" @default.
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- W3003641317 date "2019-01-01" @default.
- W3003641317 modified "2023-09-23" @default.
- W3003641317 title "PSYCHOLOGY FACTOR BASED SENTIMENT ANALYSIS FOR ONLINE PRODUCT CUSTOMER REVIEW USING MULTI-MIXED SHORT TEXT RIDGE ANALYSIS" @default.
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