Accessing Positive and Negative Online Opinions
Lecture notes in computer sciencePublished 1 January 2009
Hanhoon Kang, Seong Joon Yoo, Dongil Han
Citations7
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This study introduces some methods to classify the positive or negative review pertaining to the blog comments on a movie written in Korean by applying the traditional machine learning method for classifying literature.
Abstract
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 cafés. 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.
Keywords
Computer Science
Elsevier eBooksData Mining: Practical Machine Learning Tools and Techniques
25,718 Citations2011Ian H. Witten, Eibe Frank
Proceedings of the 24th international conference on Machine learning
11,727 Citations2007
This is an index to the papers that appear in the Proceedings of the 29th International Conference on Machine Learning (ICML-12).
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Introduction to Data Mining
7,026 Citations2008
Thumbs up?
6,987 Citations2002Bo Pang, Lillian Lee +1 more
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
A Practical Guide to Support Vector Classication
5,069 Citations2008Chih‐Wei Hsu, Chih-Chung Chang +1 more
A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
A Comparative Study on Feature Selection in Text Categorization
4,766 Citations1997Yiming Yang, Jan Pedersen
DF thresholding, the simplest method with the lowest cost in computation, can be reliably used instead of IG or CHI when the computation of these measures are too expensive, and strong correlations between the DF, IG and CHI values of a term are found.
Meeting of the Association for Computational LinguisticsThumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
3,654 Citations2002Peter Peter, Turney
Mining the peanut gallery
1,908 Citations2003Kushal Dave, Steve Lawrence +1 more
This work develops a method for automatically distinguishing between positive and negative reviews and draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation.
Choice Reviews OnlineWeb data mining: exploring hyperlinks, contents, and usage data
1,193 Citations2012
Expert Systems with ApplicationsSentiment classification of online reviews to travel destinations by supervised machine learning approaches
631 Citations2008Qiang Ye, Ziqiong Zhang +1 more
This research compared three supervised machine learning algorithms of Naive Bayes, SVM and the character based N-gram model for sentiment classification of the reviews on travel blogs for seven popular travel destinations in the US and Europe.
Expert Systems with ApplicationsA novel feature selection algorithm for text categorization
338 Citations2006Wenqian Shang, Houkuan Huang +4 more
This study designs a novel Gini index algorithm to reduce the high dimensionality of the feature space and builds a new measure function of Gini Index constructed and made to fit text categorization.
Beyond TFIDF weighting for text categorization in the vector space model
231 Citations2005Pascal Soucy, Guy W. Mineau
A new weighting method based on statistical estimation of the importance of a word for a specific categorization problem is introduced, which improves significantly the classification accuracy as measured on many categorization tasks.
IEEE Transactions on Knowledge and Data EngineeringText Document Preprocessing with the Bayes Formula for Classification Using the Support Vector Machine
217 Citations2008Dino Isa, Lam Hong Lee +2 more
This work implements an enhanced hybrid classification method through the utilization of the naive Bayes approach and the support vector machine (SVM) that shows a significant reduction in training time as compared to the Lsquare method and significant improvement in the classification accuracy when compared to pure naive BayES systems and also the TF-IDF/SVM hybrids.
A study on mutual information-based feature selectionfor text categorization
43 Citations2007Yang Xu, Gareth J. F. Jones +3 more
It is shown that the corrected MI method’s performance is similar to that of IG, and it is considerably better than PMI, and an MI method derived correctly from information theory is detail.
IEICE Transactions on Information and SystemsSVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems
17 Citations2007Hyunseong Kang, S. J. YOO
A method of applying Collaborative Filtering with a Machine Learning technique to predict users' preferences for clothes on online shopping malls when user history is insufficient is described.
An Efficient Feature Selection Using Multi-Criteria in Text Categorization
13 Citations2005Son Doan, S. Horiguchi
A novel approach in feature selection based on multicriteria ranking of features is proposed and applied to a text categorization based on a threshold value for each criterion.
Jeongbo gwahaghoe nonmunji. keompyuting ui siljeA Korean Sentence and Document Sentiment Classification System Using Sentiment Features
9 Citations2008Jaw-Won Hwang, Youngjoong Ko
This paper presents a Korean sentence and document classification system using effective sentiment features, a recent subdiscipline of text classification, concerned not with the topic but with opinion.