Accessing Positive and Negative Online Opinions

Nowadays, an increasing number of people review the comments on each item before they will purchase the commodities and services offered by online shopping malls, Internet blogs, or cafes. However, it is somewhat challenging to routinely read trough all of the comments. The purpose of this study is to introduce some methods to classify the positive or negative review pertaining to the blog comments on a movie written in Korean. For this purpose, a variety of algorithms was used to classify the reviews and allow feature-selection by applying the traditional machine learning method for classifying literature.

[1]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[2]  Wenqian Shang,et al.  A novel feature selection algorithm for text categorization , 2007, Expert Syst. Appl..

[3]  Qiang Ye,et al.  Sentiment classification of online reviews to travel destinations by supervised machine learning approaches , 2009, Expert Syst. Appl..

[4]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[5]  Dino Isa,et al.  Text Document Preprocessing with the Bayes Formula for Classification Using the Support Vector Machine , 2008, IEEE Transactions on Knowledge and Data Engineering.

[6]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[7]  Son Doan,et al.  An efficient feature selection using multi-criteria in text categorization , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[8]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[9]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[10]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[11]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[12]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[13]  Guy W. Mineau,et al.  Beyond TFIDF Weighting for Text Categorization in the Vector Space Model , 2005, IJCAI.

[14]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[15]  Seong Joon Yoo,et al.  SVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems , 2007, IEICE Trans. Inf. Syst..

[16]  Youngjoong Ko,et al.  A Korean Sentence and Document Sentiment Classification System Using Sentiment Features , 2008 .

[17]  Jintao Li,et al.  A study on mutual information-based feature selectionfor text categorization , 2007 .

[18]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[19]  Ian Witten,et al.  Data Mining , 2000 .