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Sentimental text mining based on an additional features method for text classification.

Ching-Hsue Cheng1, Hsien-Hsiu Chen1

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Summary
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This study introduces a novel sentiment analysis method using additional features and dimension reduction techniques like SVD and PCA. The approach improves accuracy and reduces processing time for analyzing customer opinions online.

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Area of Science:

  • Computer Science
  • Data Science
  • Natural Language Processing

Background:

  • The internet and social media facilitate sharing of opinions and product reviews.
  • Analyzing customer sentiment from text data is crucial for businesses.
  • Existing sentiment analysis methods may lack accuracy or efficiency.

Purpose of the Study:

  • To propose an enhanced sentiment text mining method.
  • To improve accuracy and reduce implementation time for sentiment classification.
  • To explore data dimension reduction techniques for efficiency.

Main Methods:

  • Developed a novel algorithm for preprocessing data for sentiment classification.
  • Incorporated additional features to boost sentiment classification accuracy.
  • Applied Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) for data dimension reduction.
  • Designed five modules with varying features and stemming to compare performance.

Main Results:

  • The proposed sentiment text mining method demonstrated superior accuracy compared to existing approaches.
  • The method effectively reduced implementation time for sentiment analysis.
  • Dimension reduction techniques proved beneficial for handling large datasets.

Conclusions:

  • The enhanced sentiment text mining method offers improved accuracy and efficiency.
  • The study highlights the effectiveness of additional features and dimension reduction in sentiment analysis.
  • This research provides a valuable contribution to opinion mining and customer insight extraction.