The week's pick
Random Articles
Reseach Article
Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 13 |
| Year of Publication: 2018 |
| Authors: Suryanto Nugroho, Prihandoko |
10.5120/ijca2018916916
|
Suryanto Nugroho, Prihandoko . Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification. International Journal of Computer Applications. 182, 13 ( Sep 2018), 1-5. DOI=10.5120/ijca2018916916
Abstract
The selection of West Java governor is one event that seizes the attention of the public is no exception to social media users. Public opinion on a prospective regional leader can help predict electability and tendency of voters. Data that can be used by the opinion mining process can be obtained from Twitter. Because the data is very varied form and very unstructured, it must be managed and uninformed using data pre-processing techniques into semi-structured data. This semi-structured information is followed by a classification stage to categorize the opinion into negative or positive opinions. The research methodology uses a literature study where the research will examine previous research on a similar topic. The purpose of this study is to find the right architecture to develop it into the application of twitter opinion mining to know public sentiments toward the election of the governor of west java. The result of this research is that Twitter opinion mining is part of text mining where opinions in Twitter if they want to be classified, must go through the preprocessing text stage first. The preprocessing step required from twitter data is cleansing, case folding, POS Tagging and stemming. The resulting text mining architecture is an architecture that can be used for text mining research with different topics.
References
- Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., & Liu, B. (2011). Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis. Hewlett-Packard Development Company, L.P.
- Liu, B. (2010). Sentiment Analysis and Subjectivity Handbook of Natural Language Processing, Second Edition (pp. 627-666): Chapman and Hall/CRC.
- Pang, B., & Lee, L. (2008). Subjectivity Detection and Opinion Identification. Opinion Mining and Sentiment Analysis: Now Publishers Inc. [Online].
- Wikipedia. (2017). Text Mining. Retrieved from https://en.wikipedia.org/wiki/Text_mining
- Chen, M. C., Chiu, A. L., & Chang, H. H. (2005). Mining changes in customer behaviour in retail Marketing. Expert Systems with Applications, 773–781.
- Kim, S. Y., Jung, T. S., Suh, E. H., & Hwang, H. S. (2006). Customer segmentation and strategy development based on customer lifetime value: a case study. Expert Systems with Applications 31, 101–107
- Ramya, P., & Sasirekha, S. (2014). Text Mining System For Non- Expert Miners. (IJCSIS) International Journal of Computer Science and Information Security, 12(5).
- Shelby, M. I., Warih, M., & Adiwijaya. (2013). Opinion Mining Pada Twitter Menggunakan Klasifikasi Sentimen pada Hastag berbasis Graf.
- Clayton, R. F. (2011). Coarse- and Fine-Grained Sentiment Analysis of Social Media Text. Johns Hopkins Apl Technical Digest, 30(1).
- Feldman, R., & Sanger, J. (2007). The Text Mining Handbook Advanced Approaches in Analyzing
- Pak, A., & Paroubek, P. (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining.
Index Terms
Keywords