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• W2948551194 startingPage "e0217591" @default.
• W2948551194 abstract "Owing to the emergence of the Internet and its rapid growth, people can use mobile devices on many social media platforms (blogs, Facebook forums, etc.), and the platforms provide well-known websites for people to express and share their daily activities and ideas on global issues. Many consumers utilize product review websites before making a purchase. Many well-known websites are searched for relevant product reviews and experiences of product use. We can easily collect large amounts of structured and unstructured product data and further analyze the data to determine the desired product information. For this reason, many researchers are gradually focusing on sentiment analysis or opinion exploration (opinion mining) and use this technique to extract and analyze customer opinions and emotions. This paper proposes a sentimental text mining method based on an additional features method to enhance accuracy and reduce implementation time and uses singular value decomposition and principal component analysis for data dimension reduction. This study has four contributions: (1) the proposed algorithm for preprocessing the data for sentiment classification, (2) the additional features to enhance the accuracy of the sentiment classification, (3) the application of singular value decomposition and principal component analysis for data dimension reduction, and (4) the design of five modules based on different features, with or without stemming, to compare the performance results. The experimental results show that the proposed method has better accuracy than other methods and that the proposed method can decrease the implementation time." @default.
• W2948551194 created "2019-06-14" @default.
• W2948551194 creator A5043784167 @default.
• W2948551194 creator A5048807272 @default.
• W2948551194 date "2019-06-05" @default.
• W2948551194 modified "2023-09-26" @default.
• W2948551194 title "Sentimental text mining based on an additional features method for text classification" @default.
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• W2948551194 doi "https://doi.org/10.1371/journal.pone.0217591" @default.
• W2948551194 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6550425" @default.
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• W2948551194 hasPublicationYear "2019" @default.
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