Browse > Article
http://dx.doi.org/10.7840/kics.2017.42.2.471

Investigations on Techniques and Applications of Text Analytics  

Kim, Namgyu (Kookmin University School of MIS)
Lee, Donghoon (Kookmin University The Graduate School of Business Information Technology)
Choi, Hochang (Kookmin University The Graduate School of Business Information Technology)
Wong, William Xiu Shun (Kookmin University The Graduate School of Business Information Technology)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.42, no.2, 2017 , pp. 471-492 More about this Journal
Abstract
The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.
Keywords
Big Data; Data Mining; Text Analytics; Topic Modeling;
Citations & Related Records
Times Cited By KSCI : 40  (Citation Analysis)
연도 인용수 순위
1 S. Han, "A comparative study on clustering methods for grouping related tags," J. Korean Soc. Library Inf. Sci., vol. 43, no. 3, pp. 399-416, Sept. 2009.   DOI
2 M. Steyvers and T. Griffiths, Probabilistic topic models : Handbook of latent semantic analysis, Lawrence Erlbaum Associates, 2007.
3 T. L. Griffiths and M. Steyvers, "Finding scientific topics," in Proc. National Academy Sci., vol. 101, no. suppl 1, pp. 5228-5235, Apr. 2004.   DOI
4 Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical dirichlet processes," J. Am. Statistical Assoc., vol. 101, no. 476, pp. 1566-1581, Jan. 2012.   DOI
5 Q. Yao, Z. Song, and C. Peng, "Research on text categorization based on LDA," Comput. Eng. Appl., vol. 47, no. 13, pp. 150-153, May 2011.
6 S. Y. Yu, "Exploratory study of developing a synchronization-based approach for multi-step discovery of knowledge structures," J. Inf. Sci. Theory Practice, vol. 2, no. 2, pp. 16-32, Jun. 2014.   DOI
7 M. Lim and N. Kim, "Investigating dynamic mutation process of issues using unstructured text analysis," J. Intell. Inf. Syst., vol. 22, no. 1, pp. 1-18, Mar. 2016.   DOI
8 S. A. Jin, C. E. Heo, Y. K. Jeong, and M. Song, "Topic-network based topic shift detection on twitter," J. Korean Soc. Inf. Management, vol. 30, no. 1, pp. 285-302, Mar. 2013.   DOI
9 J. H. Park and M. Song, "A study on the research trends in library & information science in korea using topic modeling," J. Korean Soc. Inf. Management, vol. 30, no. 1, pp. 7-32, Mar. 2013.   DOI
10 D. Kim and N. Kim, "Mapping categories of heterogeneous sources using text analytics," J. Intell. Inf. Syst., vol. 22, no. 4, pp. 193-215, Dec. 2016.   DOI
11 J. S. Oh, "Identifying research opportunities in the convergence of transportation and ICT using text mining techniques," J. Transport Res., vol. 22, no. 4, pp. 93-110, Dec. 2015.   DOI
12 S. T. Na, J. H. Kim, M. H. Jung, and J. E. Ahn, "Trend analysis using topic modeling for simulation studies," J. Korea Soc. Simulation, vol. 25, no. 3, pp. 107-116, Sept. 2016.   DOI
13 S. A. Jin and M. Song, "Topic modeling based interdisciplinarity measurement in the informatics related journals," J. Korean Soc. Inf. Management, vol. 33, no. 1, pp. 7-32, Mar. 2016.   DOI
14 J. Bae, N. Han and M. Song, "Twitter issue tracking system by topic modeling techniques," J. Intell. Inf. Syst., vol. 20, no. 2, pp. 109-122, Jun. 2014.   DOI
15 J. Bae, J. Son, and M. Song, "Analysis of twitter for 2012 south korea presidential election by text mining techniques," J. Intell. Inf. Syst., vol. 19, no. 3, pp. 141-156, Sept. 2013.   DOI
16 X. Wang and A. McCallum, "Topics over time: A non-Markov continuous-time model of topical trends," in Proc. 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 424-433, Philadelphia, USA, Aug. 2006.
17 D. Lee, H. Choi, and N. Kim, "A method for evaluating news value based on supply and demand of information using text analysis," J. Intell. Inf. Syst., vol. 22, no. 4, pp. 45-67, Dec. 2016.   DOI
18 H. J. Hwang, H. R. Shim, and J. Choi, "Exploration of user experience research method with big data analysis : Focusing on the online review analysis of echo," J. Korea Contents Assoc., vol. 16, no. 8, pp. 517-528, Aug. 2016.   DOI
19 G. Kim and H. Yun, "Topic modeling approach to understand changes in customer perceptions on hotel services in seoul," J. Korea Serv. Management Soc., vol. 17, no. 3, pp. 217-231, Sept. 2016.   DOI
20 J. D. Park, "A study on mapping users' topic interest for question routing for community-based q&a service," J. Korean Soc. Inf. Management, vol. 32, no. 3, pp. 397-412, Sept. 2015.   DOI
21 D. Jeong, J. Kim, G. Kim, J. U. Heo, B. W. On, and M. Kang, "A proposal of a keyword extraction system for detecting social issues," J. Intell. Inf. Syst., vol. 19, no. 3, pp. 1-23, Sept. 2013.   DOI
22 W. X. Zhao, J. Jiang, J. Weng, J. He, E. P. Lim, H. Yan, and X. Li, "Comparing twitter and traditional media using topic models," in Proc. Eur. Conf. Inf. Retrieval, pp. 338-349, Dublin, Ireland, Apr. 2011.
23 B. Noh, Z. Xu, J. Lee, D. Park, and Y. Chung, "Keyword network based repercussion effect analysis of foot-and-mouth disease using online news," J. Korean Inst. Inf. Technol., vol. 14, no. 9, pp. 143-152, Sept. 2013.
24 J. An, K. Ahn, and M. Song, "Text mining driven content analysis of ebola on news media and scientific publications," J. Korean Soc. Library Inf. Sci., vol. 50, no. 2, pp. 289- 307, May 2016.   DOI
25 J. Chang, S. Gerrish, C. Wang, J. L. Boyd-Graber, and D. M. Blei, "Reading tea leaves: How humans interpret topic models," in Proc. Advances in Neural Inf. Process. Syst., pp. 288-296, Vancouver, Canada, Dec. 2009.
26 C. Apte, F. Damerau, and S. M. Weiss, "Automated learning of decision rules for text categorization," ACM Trans. Inf. Syst. (TOIS), vol. 12, no. 3, pp. 233-251, Jul. 1994.   DOI
27 G. Weikum, "Foundations of statistical natural language processing," ACM SIGMOD, vol. 31, no. 3, pp. 37-38, Sept. 2002.
28 J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques, Elsevier, 2011.
29 F. Sebastiani, "Machine learning in automated text categorization," ACM Computing Surveys (CSUR), vol. 34, no. 1, pp. 1-47, Mar. 2002.   DOI
30 S. Dumais, J. Platt, D. Heckerman, and M. Sahami, "Inductive learning algorithms and representations for text categorization," in Proc. 7th Int. Conf. Inf. Knowledge Management, pp. 148-155, Maryland, USA, Nov. 1998.
31 V. Vapnik, Estimation of Dependences Based on Empirical Data, Springer Verlag, 1982.
32 T. Joachims, "Text categorization with support vector machines: Learning with many relevant features," in Proc. Eur. Conf. Machine Learning, pp. 137-142, London, UK, Apr. 1998.
33 J. H. Roh, H. Kim, and J. Y. Chang, "Improving hypertext classification systems through WordNet-based feature abstraction," The J. Soc. e-Business Stud., vol. 18, no. 2, pp. 95-110, May 2013.   DOI
34 Y. H. Kang and Y. B. Park, "Design of automatic document classifier for IT documents based on SVM," J. IKEEE, vol. 8, no. 2, pp. 186-194, Dec. 2012.
35 C. Lee, S. Lim, and H. Kim, "Korean semantic role labeling using structured SVM," J. KIISE, vol. 42, no. 2, pp. 220-226, Feb. 2015.   DOI
36 D. D. Lewis and M. Ringuette, "A comparison of two learning algorithms for text categorization," in Proc. 3rd Annu. Symp. Document Anal. Inf. Retrieval, pp. 81-93, Las Vegas, USA, Apr. 1994.
37 K. P. Kim and Y. S. Kwon, "Performance comparison of naive bayesian learning and centroid-based classification for e-mail classification," IE Interfaces, vol. 18, no. 1, pp. 10-21, Mar. 2005.
38 J. Yoon, J. Lee, and D. W. Kim, "Feature selection in multi-label classification using NSGA-II algorithm," J. KISS : Softw. Appl., vol. 40, no. 3, pp. 133-140, Mar. 2013.
39 M. Kam and M. Song, "A study on differences of contents and tones of arguments among newspapers using text mining analysis," J. Intell. Inf. Syst., vol. 18, no. 3, pp. 53-77, Sept. 2012.   DOI
40 H. Lim and D. W. Kim, "Using mutual information for selecting features in multi-label classification," J. KISS : Softw. Appl., vol. 39, no. 10, pp. 806-811, Oct. 2012.
41 J. S. Hong, N. Kim, and S. Lee, "A methodology for automatic multi - categorization of single - categorized documents," J. Intell. Inf. Syst., vol. 20, no. 3, pp. 77-92, Sept. 2014.   DOI
42 J. W. Seo, T. S. Shon, J. T. Seo, and J. S. Moon, "A study on the filtering of spam e-mail using n-Gram indexing and support vector machine," J. Korea Inst. Inf. Security & Cryptology, vol. 14, no. 2, pp. 23-33, Apr. 2004.
43 H. J. Kim, J. J. Jung, and G. S. Jo, "Spam - mail filtering system using weighted bayesian classifier," J. KISS : Softw. Appl., vol. 31, no. 8, pp. 1092-1100, Aug. 2004.
44 I. Joe and H. T. Shim, "A SVM-based spam filtering system for short message service (SMS)," The J. Korean Inst. Commun. Inf. Sci., vol. 34, no. 9, pp. 908-913, Sept. 2009.
45 M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, "A bayesian approach to filtering junk e-mail," in Proc. AAAI Workshop on Learning for Text Categorization, pp. 55-62, Wisconsin, USA, Jul. 1998.
46 X. Jia, K. Zheng, W. Li, T. Liu, and L. Shang, "Three-way decisions solution to filter spam email: An empirical study," in Proc. Int. Conf. Rough Sets and Current Trends in Comput., pp. 287-296, Chengdu, China, Aug. 2012.
47 B. Liu, "Sentiment analysis and opinion mining," Synthesis Lectures on Human Lang. Technol., vol. 5, no. 1, pp. 1-167, May. 2012.   DOI
48 H. S. Lee, J. I. Cho, M. H. Jung, and J. S. Moon, "An approach to detect spam e-mail with abnormal character composition," J. Korea Inst. Inf. Security & Cryptology, vol. 18, no. 6A, pp. 129-137, Dec. 2008.
49 S. Lee, "A splog detection system using support vector systems," J. Korea Inst. Inf. Commun. Eng., vol. 15, no. 1, pp. 163-168, Jan. 2011.   DOI
50 J. Jung and M. Yoo, "Tag search system using the keyword extraction and similarity evaluation," The J. Korean Inst. Commun. Inf. Sci., vol. 40, no. 12, pp. 2458-2487, Dec. 2015.
51 B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: Sentiment classification using machine learning techniques," in Proc. ACL- 02 Conf. Empirical Methods in Natural Lang. Process.-Volume 10, pp. 79-86, Philadelphia, USA, Jul. 2002.
52 P. D. Turney, "Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews," in Proc. 40th Annu. Meeting Assoc. Computational Linguistics, pp. 417-424, Philadelphia, USA, Jul. 2002.
53 K. S. Jones, "A statistical interpretation of term specificity and its application in retrieval," J. Documentation, vol. 28, no. 1, pp. 11-21, Jan. 1972.   DOI
54 G. Salton, The SMART retrieval system-experiments in automatic document processing, Prentice-Hall, 1971.
55 G. Salton, A. Wong, and C. S. Yang, "A vector space model for automatic indexing," Commun. ACM, vol. 18, no. 11, pp. 613-620, Nov. 1975.   DOI
56 H. P. Luhn, "A statistical approach to mechanized encoding and searching of literary information," IBM J. Res. Develop., vol. 1, no. 4, pp. 309-317, Oct. 1957.   DOI
57 G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Inf. Process. & Management, vol. 24, no. 5, pp. 513-523, Dec. 1988.   DOI
58 D. L. Lee, H. Chuang and K. Seamons, "Document ranking and the vector-space model," IEEE Softw., vol. 14, no. 2, pp. 67-75, Mar. 1997.   DOI
59 K. Pearson, "On lines and planes of closest fit to systems of point in space," Philosophical Mag., vol. 2, pp. 559-572, 1901.   DOI
60 H. Hotelling, "Analysis of a complex of statistical variables into principal components," J. Educational Psychol., vol. 24, no.6, pp. 417, Sept. 1933.   DOI
61 J. E. Jackson, A User's Guide to Principal Components, John Wiley & Sons, 2005.
62 G. Saporta and N. Niang, Principal component analysis: application to statistical process control, Data Analysis, 2009.
63 G. W. Stewart, "On the early history of the singular value decomposition," SIAM Rev., vol. 35, no. 4, pp. 551-566, Dec. 1993.   DOI
64 S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by latent semantic analysis," J. Am. Soc. Inf. Sci., vol. 41, no. 6, pp. 391-407, Sept. 1990.   DOI
65 J. M. Wiebe, R. F. Bruce, and T. P. O'Hara, "Development and use of a gold-standard data set for subjectivity classifications," in Proc. 37th Annu. Meeting of the Assoc. Computational Linguistics on Computational Linguistics, pp. 246-253, College Park, USA, Jun. 1999.
66 M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. 10th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 168-177, Seattle, USA, Aug. 2004.
67 D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, Oct. 1999.   DOI
68 D. L. Lee, H. Chuang, and K. Seamons, "Document ranking and the vector-space model," IEEE Softw., vol. 14, no. 2, pp. 67-75, Mar. 1997.   DOI
69 M. B. Koll, "WEIRD: An approach to concept-based information retrieval," ACM SIGIR Forum, vol. 13, no. 4, pp. 32-50, Apr. 1979.
70 T. Hofmann, "Probabilistic latent semantic indexing," in Proc. 22nd Annu. Int. ACM SIGIR Conf. Research and Development in Inf. Retrieval, pp. 50-57, Berkeley, USA, Aug. 1999.
71 D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," J. Machine Learning Res., vol. 3, pp. 993-1022, Jan. 2003.
72 I. Jolliffe, Principal Component Analysis, John Wiley & Sons, 2002.
73 T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint, arXiv:1301.3781, Jan. 2013.
74 Y. Goldberg and O. Levy, "Word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method," arXiv preprint, arXiv:1402.3722, Feb. 2014.
75 O. N. Park and H. J. Park, "A study on the international research trends in electronic records management: InterPARES 3 and ITrust achievements," J. Records Management & Archives Soc. Korea, vol. 16, no. 1, pp. 89-120, Feb. 2016.   DOI
76 S. JU and M. S. Myoung, "The analysis of core contents in consumer area from 1st to 2009 revised middle school home economics textbooks," J. Korean Home Econ. Edu. Assoc., vol. 27, no. 4, pp. 37-50, Dec. 2015.   DOI
77 S. M. Kim and E. Hovy, "Determining the sentiment of opinions," in Proc. 20th Int. Conf. Computational Linguistics. Association for Computational Linguistics, pp. 1367-1367, Geneva, Switzerland, Aug. 2004.
78 H. Chen and D. Zimbra, "AI and opinion mining," IEEE Intell. Syst., vol. 25, no. 3, pp. 74-80, May 2010.   DOI
79 N. Jindal and B. Liu, "Mining comparative sentences and relations," in Proc. AAAI, pp. 1331-1336, Boston, USA, Jul. 2006.
80 J. Kamps, M. J. Marx, R. J. Mokken, and M. Rijke, "Using wordnet to measure semantic orientations of adjectives," in Proc. LREC 2004, pp. 1115-1118, Lisbon, Portugal, May 2004.
81 V. Hatzivassiloglou and K. R. McKeown, "Predicting the semantic orientation of adjectives," in Proc. 8th Conf. Eur. Chapter of the Assoc. Computational Linguistics, pp. 147- 181, Madrid, Spain, Jul. 1997.
82 X. Ding, B. Liu, and L. Zhang, "Entity discovery and assignment for opinion mining applications," in Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 1125-1134, Paris, France, Jul. 2009.
83 E. Yu, Y. Kim, N. Kim, and S. R. Jeong, "Predicting the direction of the stock index by using a domain-specific sentiment dictionary," J. Intell. Inf. Syst., vol. 19, no. 1, pp. 95-110, Mar. 2013.
84 E. C. Dragut, H. Wang, P. Sistla, C. Yu, and W. Meng, "Polarity consistency checking for domain independent sentiment dictionaries," in Proc. 50th Annu. Meeting of the Assoc. for Computational Linguistics: Long Papers- Volume 1, pp. 997-1005, Jeju Island, Korea, Jul. 2012.
85 W. Seo, H. Park, and J. Yoon, "An exploratory study on the korean national R&D trends using co-word analysis," J. Inf. Technol. Appl. & Management, vol. 19, no. 4, pp. 1-18, Dec. 2012.
86 V. Ingle and S. Deshmukh, "Live news streams extraction for visualization of stock market trends," in Proc. Int. Conf. Sign., Netw., Comput., Syst., pp. 297-301, New Delhi, India, Feb. 2016.
87 W. Cui, Y. Wu, S. Liu, F. Wei, M. X. Zhou, and H. Qu, "Context-preserving, dynamic word cloud visualization," IEEE Comput. Graphics Appl., vol. 30, no. 6, pp. 42-53, Nov. 2010.   DOI
88 D. Scanfeld, V. Scanfeld, and E. L. Larson, "Dissemination of health information through social networks: Twitter and antibiotics," Am. J. Infection Control, vol. 38, no. 3, pp. 182-188, Apr. 2010.   DOI
89 G. E. Heo and M. Song, "Examining the intellectual structure of a medical informatics journal with author co-citation analysis and co-word analysis," J. Korean Soc. Inf. Management, vol. 30, no. 2, pp. 107-225, Jun. 2013.
90 S. Seo and E. Chung, "Domain analysis on the field of open access by co-word analysis," J. Korean Biblia Soc. Library Inf. Sci., vol. 24, no. 1, pp. 207-228, Mar. 2013.   DOI
91 B. Kang and J. H. Park, "Profiling and co-word analysis of teaching korean as a foreign language domain," J. Korean Soc. Inf. Management, vol. 30, no. 4, pp. 195-213, Dec. 2013.   DOI
92 H. Choi and H. Varian, "Predicting the present with google trends," Econ. Record, vol. 88, no. 1, pp. 2-9, Jun. 2012.   DOI
93 Y. Jung, Y. Choi, and S. H. Myaeng, "Determining mood for a blog by combining multiple sources of evidence," in Proc. IEEE/WIC/ACM Web Intell., Int. Conf., pp. 271-274, California, USA, Nov. 2007.
94 S. Park, W. Lee, and I. C. Moon, "Efficient extraction of domain specific sentiment lexicon with active learning," Pattern Recognition Lett., vol. 56, no. 15, pp. 38-44, Apr. 2015.   DOI
95 N. Askitas and K. F. Zimmermann, "Google econometrics and unemployment forecasting," Appl. Econ. Quarterly, vol. 55, no. 2, pp. 107-120, Jun. 2009.   DOI
96 A. Go, R. Bhayani, and L. Huang, Twitter sentiment classification using distant supervision, CS224N Project Report, Stanford, 2009.
97 D. Davidov, O. Tsur, and A. Rappoport, "Enhanced sentiment learning using twitter hashtags and smileys," in Proc. 23rd Int. Conf. Computational Linguistics: Posters, pp. 241-249, Beijing, China, Aug. 2010.
98 X. Wang, F. Wei, X. Liu, M. Zhou, and M. Zhang, "Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach," in Proc. 20th ACM Int. Conf. Information and Knowledge Management, pp. 1031-1040, Glasgow, UK, Oct. 2011.
99 J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," J. Computational Sci., vol. 2, no. 1, pp. 1-8, Mar. 2011.   DOI
100 J. S. Lim and J. M. Kim, "An empirical comparison of machine learning models for classifying emotions in korean twitter," J. Korea Multimedia Soc., vol. 17, no. 2, pp. 232-239, Feb. 2014.   DOI
101 F. Keshtkar and D. Inkpen, "Using sentiment orientation features for mood classification in blogs," in Proc. IEEE NLP-KE 2009, pp. 1-6, Dalian, China, Sept. 2009.
102 K. H. Y. Lin, C. Yang, and H. H. Chen, "Emotion classification of online news articles from the reader's perspective," in Proc. 2008 IEEE/WIC/ACM Int. Conf. Web Intell. and Intell. Agent Technol.-Volume 01, pp. 220-226, Washington, USA, Dec. 2008.
103 M. Nam, E. Lee, and J. Shin, "A method for user sentiment classification using instagram hashtags," J. Korea Multimedia Soc., vol. 18, no. 11, pp. 1391-1399, Nov. 2015.   DOI
104 H. Steinhaus, "Sur la division des corps materiels en parties," Bull. Acad. Polon. Sci., vol. 4, no. 12, pp. 801-804, Oct. 1956.
105 N. Khanh-Ly, B. J. Shin, and S. J. Yoo, "Hot topic detection and technology trend tracking for patents utilizing term frequency and proportional document frequency and semantic information," in Proc. BigComp, pp. 223-230, Hong Kong, China, Jan. 2016.
106 R. Kaushik, S. A. Chandra, D. Mallya, J. N. V. K. Chaitanya and S. S. Kamath, "Sociopedia: An interactive system for event detection and trend analysis for twitter data," in Proc. Int. Conf. Advanced Comput., Netw. Informatics, pp. 63-70, Bhubaneswar, India, Sept. 2015.
107 J. B. Yi, C. K. Lee, and K. J. CHA, "An analysis of IT trends using tweet data," J. Intell. Inf. Syst., vol. 21, no. 1, pp. 143-159, Mar. 2015.   DOI
108 J. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proc. 5th Berkeley Symp. Mathematical Statistics Probability, vol. 1, no. 14, pp. 281-297, Berkeley, USA, Jun. 1967.
109 K. Bae, J. Hwang, Y. Ko, and J. Kim, "A search-result clustering method based on word clustering for effective browsing of the paper retrieval results," J. KISS : Software and Appl., vol. 37, no. 3, pp. 214-221, Mar. 2010.
110 S. Jung, S. H. Lim, J. H. Jeon, B. M. Kim, and H. A. Lee, "Web search result clustering using snippets," J. KISS : Databases, vol. 39, no. 5, pp. 321-331, Oct. 2012.
111 J. H. Kim, J. S. Lee, M. Lee, W. Kim, and J. S. Hong, "Term mapping methodology between everyday words and legal terms for law information search system," J. Intell. Inf. Syst., vol. 18, no. 3, pp. 137-152, Sept. 2012.   DOI
112 C. Graeme, "Googling the present," The Labour Gazette, vol. 4, no. 12, pp. 59-95, Dec. 2010.
113 J. W Hwang and Y. Ko, "A korean sentence and document sentiment classification system using sentiment features," J. KIISE : Comput. Practices and Lett., vol. 14, no. 3, pp. 336-340, May 2008.
114 T. N. Phan and M. Yoo, "Facebook fan page evaluation system based on user opinion mining," The J. Korean Inst. Commun. and Inf. Sci., vol. 40, no. 12, pp. 2488-2490, Dec. 2015.   DOI
115 Y. Kim and M. Song, "A study on analyzing sentiments on movie reviews by multi-level sentiment classifier," J. Intell. Inf. Syst., vol. 22, no. 3, pp. 71-89, Sept. 2016.   DOI
116 C. Lee, D. Choi, S. Kim, and J. Kang, "Classification and analysis of emotion in korean microblog texts," J. KISS : Databases, vol. 40, no. 3, pp. 159-167, Jun. 2013.
117 J. An, J. Bae, N. Han, and M. Song, "A study of 'Emotion Trigger' by text mining techniques," J. Intell. Inf. Syst., vol. 21, no. 2, pp. 69-92, Jun. 2015.   DOI
118 J. Moon, I. Jang, Y. C. Choe, J. G. Kim, and G. Bock, "Case study of big data-based agri-food recommendation system according to types of customers," The J. Korean Inst. Commun. Inf. Sci., vol. 40, no. 5, pp. 903-913, May 2015.   DOI
119 D. Kim, W. X. S. Wong, M. Lim, C. Liu, N. Kim, J. Park, W. Kil, and H. Yoon, "A methodology for analyzing public opinion about science and technology issues using text analysis," J. Inf. Technol. Serv., vol. 14, no. 3, pp. 33-48, Sept. 2015.
120 S. Byun, D. Lee, and N. Kim, "A methodology for identifying issues of user reviews from the perspective of evaluation criteria: Focus on a hotel information site," J. Intell. Inf. Syst., vol. 22, no. 3, pp. 23-43, Sept. 2016.   DOI