Cited By
View all- Cheng CChen H(2019)Sentimental text mining based on an additional features method for text classificationPLOS ONE10.1371/journal.pone.021759114:6(e0217591)Online publication date: 5-Jun-2019
A Text Mining Based on Refined Feature Selection to Predict Sentimental Review
Abstract
This paper proposed an additional feature set and reduced the data dimension by SVD and PCA in order to increase accuracy and decrease executing time in text mining. The contribution of this study has: (i) proposed a preprocessing algorithm for sentiment classification, (ii) refined a feature set by adding adjective and adverb feature for sentiment classification, and (iii) utilized SVD then PCA to reduce data dimension for manager identified the sentimental labels. The experimental results show that the proposed model can obtain the better accuracy and the additional features make the better performance. Moreover, the dimension reduction can reduce the executing time effectively.
References
[1]
Medhat, W.H., Ahmed; Korashy, Hoda, 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 1093--1113.
[2]
Li, Y.-M.L., Tsung-Yung, 2013. Deriving market intelligence from microblogs. Decision Support Systems(4), 206--217.
[3]
Kang, D.P., Yongtae, 2014. Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications(3), 1041--1050.
[4]
Rui, H.L., Yizao; Whinston, Andrew, 2013. Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems(11), 863--870.
[5]
Xiao, S.W., Chih-Ping; Dong, Ming, 2015. Crowd intelligence: Analyzing online product reviews for preference measurement. Information & Management.
[6]
Archak, N.G., Anindya; Ipeirotis, Panagiotis G., 2011. Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Management Science, 1485--1509.
[7]
Ravi, K.R., Vadlamani, 2015. A survey on opinion mining and sentiment analysis: Tasks, approache and applications. Knowledge-Based Systems(6), 14--46.
[8]
Liu, B., 2010. Sentiment Analysis: A Multi-Faceted Problem. IEEE Intelligent Systems, 76--80.
[9]
Abbasi, A.F., S.; Zhang, Zhu; Chen, Hsinchun, 2011. Selecting Attributes for Sentiment Classification Using Feature Relation Networks. IEEE Transactions on Knowledge and Data Engineering, 447--462.
[10]
Kang, B., Lee. K; Choe, J. 2016. Improvement of ensemble smoother with SVD-assisted sampling scheme. Journal of Petroleum Science and Engineering(2), 114--124.
[11]
Yu, X.C., Pharino; Sim, Kwee-Bo, 2014. Analysis the effect of PCA for feature reduction in non-stationary EEGbased motor imagery of BCI system. Optik, 1498--1502.
[12]
Lewis, D.D., 1998. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In Proc. of the Eurpean Conference on Machine Learning (ECML).
[13]
Nigam, K., Lafferty, J., & McCallum, A. (1999). Using maximum entropy for text classification. Proc. of the IJCAI-99 Workshop on Machine Learning for Information Filtering, (pp. 61--67).
[14]
Rahate, R.S.M., Emmanuel, 2013. Feature Selection for Sentiment Analysis by using SVM. International Journal of Computer Applications, 24--32.
[15]
Vapnik, V., The Nature of Statistical Learning Theory, 1995 (Springer: New York).
[16]
Breiman, L. (2001, 10). Random forests. Maching Learning, pp. 5--32.
[17]
Pang, B.L., Lillian; Vaithyanathan, Shivakumar, 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing.
[18]
Moraes, R.V., Jo O Francisco; Neto, Wilson P. Gavi O, 2013. Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 621--633.
[19]
Pang, B.L., Lillian, 2004. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. In Proceedings of the 42nd Annual.
[20]
Turney; D., P., 2002. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In ACL.
Recommendations
Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values
Each type of classifier has its own advantages as well as certain shortcomings. In this paper, we take the advantages of the associative classifier and the Naive Bayes Classifier to make up the shortcomings of each other, thus improving the accuracy of ...
Feature and Opinion Mining for Customer Review Summarization
PReMI '09: Proceedings of the 3rd International Conference on Pattern Recognition and Machine IntelligenceIn this paper, we present an opinion mining system to identify product features and opinions from review documents. The features and opinions are extracted using semantic and linguistic analysis of text documents. The polarity of opinion sentences is ...
Helmholtz principle based supervised and unsupervised feature selection methods for text mining
Highlights- We propose new supervised and unsupervised feature selection methods, called Meaning Based Feature Selection (MBFS), for feature selection in text ...
AbstractOne of the important problems in text classification is the high dimensionality of the feature space. Feature selection methods are used to reduce the dimensionality of the feature space by selecting the most valuable features for ...
Comments
Information & Contributors
Information
Published In
December 2016
343 pages
ISBN:9781450347938
DOI:10.1145/3033288
Copyright © 2016 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 December 2016
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICNCC '16
ICNCC '16: Fifth International Conference on Network, Communication and Computing
December 17 - 21, 2016
Kyoto, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 114Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025
Other Metrics
Citations
Cited By
View all- Cheng CChen H(2019)Sentimental text mining based on an additional features method for text classificationPLOS ONE10.1371/journal.pone.021759114:6(e0217591)Online publication date: 5-Jun-2019
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in