A study of feature exraction techniques for classifying topics and sentiments from news posts

Recently, many news channels have their own Facebook pages in which news posts have been released in a daily basis. Consequently, these news posts contain temporal opinions about social events that may change over time due to external factors as well as may use as a monitor to the significant events...

Full description

Saved in:

Bibliographic Details
Main Author:	Al-Dyani, Wafa Zubair Abdullah
Format:	Thesis
Language:	eng eng
Published:	2014
Subjects:	T58.5-58.64 Information technology
Online Access:	https://etd.uum.edu.my/5618/1/s814383_01.pdf https://etd.uum.edu.my/5618/2/s814383_02.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.5618
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Kabir Ahmad, Farzana
topic	T58.5-58.64 Information technology
spellingShingle	T58.5-58.64 Information technology Al-Dyani, Wafa Zubair Abdullah A study of feature exraction techniques for classifying topics and sentiments from news posts
description	Recently, many news channels have their own Facebook pages in which news posts have been released in a daily basis. Consequently, these news posts contain temporal opinions about social events that may change over time due to external factors as well as may use as a monitor to the significant events happened around the world. As a result, many text mining researches have been conducted in the area of Temporal Sentiment Analysis, which one of its most challenging tasks is to detect and extract the key features from news posts that arrive continuously overtime. However, extracting these features is a challenging task due to post’s complex properties, also posts about a specific topic may grow or vanish overtime leading in producing imbalanced datasets. Thus, this study has developed a comparative analysis on feature extraction Techniques which has examined various feature extraction techniques (TF-IDF, TF, BTO, IG, Chi-square) with three different n-gram features (Unigram, Bigram, Trigram), and using SVM as a classifier. The aim of this study is to discover the optimal Feature Extraction Technique (FET) that could achieve optimum accuracy results for both topic and sentiment classification. Accordingly, this analysis is conducted on three news channels’ datasets. The experimental results for topic classification have shown that Chi-square with unigram have proven to be the best FET compared to other techniques. Furthermore, to overcome the problem of imbalanced data, this study has combined the best FET with OverSampling technology. The evaluation results have shown an improvement in classifier’s performance and has achieved a higher accuracy at 93.37%, 92.89%, and 91.92 for BBC, Al-Arabiya, and Al-Jazeera, respectively, compared to what have been obtained on original datasets. Similarly, same combination (Chi-square+Unigram) has been used for sentiment classification and obtained accuracies at rates of 81.87%, 70.01%, 77.36%. However, testing the recognized optimal FET on unseen randomly selected news posts has shown a relatively very low accuracies for both topic and sentiment classification due to the changes of topics and sentiments over time.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Al-Dyani, Wafa Zubair Abdullah
author_facet	Al-Dyani, Wafa Zubair Abdullah
author_sort	Al-Dyani, Wafa Zubair Abdullah
title	A study of feature exraction techniques for classifying topics and sentiments from news posts
title_short	A study of feature exraction techniques for classifying topics and sentiments from news posts
title_full	A study of feature exraction techniques for classifying topics and sentiments from news posts
title_fullStr	A study of feature exraction techniques for classifying topics and sentiments from news posts
title_full_unstemmed	A study of feature exraction techniques for classifying topics and sentiments from news posts
title_sort	study of feature exraction techniques for classifying topics and sentiments from news posts
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2014
url	https://etd.uum.edu.my/5618/1/s814383_01.pdf https://etd.uum.edu.my/5618/2/s814383_02.pdf
_version_	1747827958164226048
spelling	my-uum-etd.56182022-04-09T23:28:04Z A study of feature exraction techniques for classifying topics and sentiments from news posts 2014 Al-Dyani, Wafa Zubair Abdullah Kabir Ahmad, Farzana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences T58.5-58.64 Information technology Recently, many news channels have their own Facebook pages in which news posts have been released in a daily basis. Consequently, these news posts contain temporal opinions about social events that may change over time due to external factors as well as may use as a monitor to the significant events happened around the world. As a result, many text mining researches have been conducted in the area of Temporal Sentiment Analysis, which one of its most challenging tasks is to detect and extract the key features from news posts that arrive continuously overtime. However, extracting these features is a challenging task due to post’s complex properties, also posts about a specific topic may grow or vanish overtime leading in producing imbalanced datasets. Thus, this study has developed a comparative analysis on feature extraction Techniques which has examined various feature extraction techniques (TF-IDF, TF, BTO, IG, Chi-square) with three different n-gram features (Unigram, Bigram, Trigram), and using SVM as a classifier. The aim of this study is to discover the optimal Feature Extraction Technique (FET) that could achieve optimum accuracy results for both topic and sentiment classification. Accordingly, this analysis is conducted on three news channels’ datasets. The experimental results for topic classification have shown that Chi-square with unigram have proven to be the best FET compared to other techniques. Furthermore, to overcome the problem of imbalanced data, this study has combined the best FET with OverSampling technology. The evaluation results have shown an improvement in classifier’s performance and has achieved a higher accuracy at 93.37%, 92.89%, and 91.92 for BBC, Al-Arabiya, and Al-Jazeera, respectively, compared to what have been obtained on original datasets. Similarly, same combination (Chi-square+Unigram) has been used for sentiment classification and obtained accuracies at rates of 81.87%, 70.01%, 77.36%. However, testing the recognized optimal FET on unseen randomly selected news posts has shown a relatively very low accuracies for both topic and sentiment classification due to the changes of topics and sentiments over time. 2014 Thesis https://etd.uum.edu.my/5618/ https://etd.uum.edu.my/5618/1/s814383_01.pdf text eng public https://etd.uum.edu.my/5618/2/s814383_02.pdf text eng public masters masters Universiti Utara Malaysia [1] J. Akaichi, Z. Dhouioui, and M. J. Lopez-Huertas Perez, “Text mining facebook status updates for sentiment classification,” in System Theory, Control and Computing (ICSTCC), 2013 17th International Conference, 2013, pp. 640–645. [2] E. N. Neumann, The spiral of silence Public opinion–our social skin. Chicago: University of Chicago Press, 1993. [3] E. Bjørkelund and T. Burnett, “Temporal Opinion Mining,” no. June, p. 122, 2012. [4] T. Fukuhara, H. Nakagawa, and T. Nishida, “Understanding Sentiment of People from News Articles: Temporal Sentiment Analysis of Social Events.,” in ICWSM, 2007. [5] R. Chakraborty, “DOMAIN KEYWORD EXTRACTION TECHNIQUE : A NEW WEIGHTING METHOD,” pp. 109–118, 2013. [6] F. Neri, C. Aliprandi, F. Capeci, M. Cuadros, and T. By, “Sentiment Analysis on Social Media,” 2012 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min., pp. 919–926, 2012. [7] H. Bacan, I. S. Pandzic, and D. Gulija, “Automated news item categorization,” in Proceedings of the 19th Annual Conference of The Japanese Society for Artificial Intelligence, 2005, pp. 251–256. [8] J. Zhang, Y. Kawai, S. Nakajima, Y. Matsumoto, and K. Tanaka, “Sentiment bias detection in support of news credibility judgment,” in System Sciences (HICSS), 2011 44th Hawaii International Conference on, 2011, pp. 1–10. [9] J. Allan, “Introduction to topic detection and tracking,” in Topic detection and tracking, Springer, 2002, pp. 1–16. [10] D. Clarke, P. Lane, and P. Hender, “Developing robust models for favourability analysis,” in Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, 2011, pp. 44–52. [11] P. D. Turney, “Thumbs Up or Thumbs Down ? Semantic Orientation Applied to Unsupervised Classification of Reviews,” no. July, pp. 417–424, 2002. [12] H. Uğuz, “A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm,” Knowledge-Based Syst., vol. 24, no. 7, pp. 1024–1032, Oct. 2011. [13] K. Lee, D. Palsetia, R. Narayanan, M. M. A. Patwary, A. Agrawal, and A. Choudhary, “Twitter trending topic classification,” Proc. - IEEE Int. Conf. Data Mining, ICDM, pp. 251–258, 2011. [14] Z. Fu, X. Sun, J. Shu, and L. Zhou, “Plain Text Zero Knowledge Watermarking Detection Based on Asymmetric Encryption,” vol. 48, no. Cia, pp. 126–134, 2014. [15] L.-W. Ku, Y.-T. Liang, and H.-H. Chen, “Opinion Extraction, Summarization and Tracking in News and Blog Corpora.,” in AAAI spring symposium: Computational approaches to analyzing weblogs, 2006, vol. 100107. [16] I. P. Cvijikj and F. Michahelles, “Monitoring trends on Facebook,” Proc. - IEEE 9th Int. Conf. Dependable, Auton. Secur. Comput. DASC 2011, pp. 895–902, 2011. [17] M. Cataldi, U. Torino, L. Di Caro, U. Torino, C. Schifanella, and U. Torino, “a4-Cataldi,” 2010. [18] J. Weng, Y. Yao, E. Leonardi, F. Lee, and B. Lee, “Event Detection in Twitter Event Detection in Twitter ∗ ,” 2011. [19] G. Burnside, D. Milioris, and P. Jacquet, “One Day in Twitter: Topic Detection Via Joint Complexity,” Www, 2014. [20] S. Greener and A. Rospigliosi, ePub - European Conference on Social Media: ECSM, vol. 7. Academic Conferences Limited, 2014. [21] D. Richter, P. D. D. K. Riemer, and J. vom Brocke, “Internet social networking,” Wirtschaftsinformatik, vol. 53, no. 2, pp. 89–103, 2011. [22] S. Setty, R. Jadi, S. Shaikh, C. Mattikalli, and U. Mudenagudi, “Classification of facebook news feeds and sentiment analysis,” in Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on, 2014, pp. 18–23. [23] J. K. Ahkter and S. Soria, “Sentiment analysis: Facebook status messages,” Unpubl. master’s thesis, Stanford, CA, 2010. [24] A. E.-D. A. Hamouda and F. E. El-taher, “Sentiment Analyzer for Arabic Comments System,” Int. J. Adv. Comput. Sci. Appl., vol. 4, no. 3, 2013. [25] N. Bansal and N. Koudas, “BlogScope: a system for online analysis of high volume text streams,” Proc. 33rd Int. Conf. Very large data bases, pp. 1410–1413, 2007. [26] H. Choi and H. Varian, “Predicting the present with google trends,” Econ. Rec., vol. 88, no. s1, pp. 2–9, 2012. [27] T. Nguyen, D. Phung, B. Adams, and S. Venkatesh, “Event extraction using behaviors of sentiment signals and burst structure in social media,” Knowl. Inf. Syst., vol. 37, no. February, pp. 279–304, 2013. [28] S. Bai, Y. Ning, S. Yuan, and T. Zhu, “Predicting Reader ’ s Emotion,” pp. 16–27, 2013. [29] B. Thomas, “Exploration of Robust Features for Multiclass Emotion Classification,” pp. 1704–1709, 2014. [30] J. Zhang, Y. Kawai, and T. Kumamoto, “Extracting Similar and Opposite News Websites Based on Sentiment Analysis,” in Proc. of International Conference on Industrial and Intelligent Information (ICIII 2012), 2012, pp. 24–29. [31] L. U. Ye and R. Xu, “E Motion Prediction of News Articles From Reader’S Perspective Based on Multi-Label Classi Fication,” pp. 15–17, 2012. [32] C. G. Patil, “Use of Porter Stemming Algorithm and SVM for Emotion Extraction from News Headlines,” vol. 2, no. 7, pp. 9–13. [33] G. Li and F. Liu, “A clustering-based approach on sentiment analysis,” Proc. 2010 IEEE Int. Conf. Intell. Syst. Knowl. Eng. ISKE 2010, pp. 331–337, 2010. [34] J. Kamps, M. J. Marx, R. J. Mokken, and M. De Rijke, “Using wordnet to measure semantic orientations of adjectives,” 2004. [35] J. Staiano and M. Guerini, “DepecheMood: a Lexicon for emotion analysis from crowd-annotated news,” arXiv Prepr. arXiv1405.1605, 2014. [36] R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to imbalanced datasets,” in Machine Learning: ECML 2004, Springer, 2004, pp. 39–50. [37] F. Chang, J. Guo, W. Xu, and K. Yao, “A Feature Selection Method to Handle Imbalanced Data in Text Classification.,” J. Digit. Inf. Manag., vol. 13, no. 3, p. 169, 2015. [38] A. Zughrat, M. Mahfouf, Y. Y. Yang, and S. Thornton, “Support Vector Machines for Class Imbalance Rail Data Classification with Bootstrappingbased Over-Sampling and Under-Sampling,” in 19th World Congress of the International Federation of Automatic Control. Cape Town, 2014. [39] P. G. Preethi and V. Uma, “Temporal Sentiment Analysis and Causal Rules Extraction from Tweets for Event Prediction,” Procedia Comput. Sci., vol. 48, pp. 84–89, 2015. [40] A. Balahur, R. Steinberger, M. Kabadjov, V. Zavarella, E. Van Der Goot, M. Halkia, B. Pouliquen, and J. Belyaeva, “Sentiment analysis in the news,” Proc. Seventh Int. Conf. Lang. Resour. Eval., pp. 2216–2220, 2010. [41] S.-M. Kim and E. Hovy, “Extracting opinions, opinion holders, and topics expressed in online news media text,” in Proceedings of the Workshop on Sentiment and Subjectivity in Text, 2006, pp. 1–8. [42] R. H. W. Pinheiro, G. D. C. Cavalcanti, R. F. Correa, and T. I. Ren, “A globalranking local feature selection method for text categorization,” Expert Syst. Appl., vol. 39, no. 17, pp. 12851–12857, 2012. [43] “Top News on Facebook \| Fan Page List.” [Online]. Available: http://fanpagelist.com/ category/news/view/list/sort/fans/page1. [Accessed: 06-Dec-2015]. [44] “BBC World News achieves major distribution milestone, reaching more than 330m households worldwide,” 2012. [45] “About BBC World News TV,” 2011. [46] “Media Use in the Middle East 2013' Northwestern University in Qatar.” [Online]. Available: http://menamediasurvey.northwestern. edu/. [Accessed: 09-Dec-2015]. [47] T. Johnson and S. Fahmy, “Who is winning the hearts and minds of the Arab public?,” Int. Commun. Res. J., vol. 45, no. 1–2, pp. 24–48, 2010. [48] “Major Events in 2014, What Happened in 2014.” [Online]. Available: http://www.mapsofworld.com/events/year-2014/. [Accessed: 09-Dec-2015]. [49] “The Biggest News Stories of 2014 - ABC News.” [Online]. Available: http://abcnews.go.com/International/biggest-news-stories-2014/story?id=27466867. [Accessed: 09-Dec-2015]. [50] “The 10 Biggest International Stories of 2014 - The Atlantic.” [Online]. Available: http://www.theatlantic.com/international/archive/2014/12/the-10-biggest-international-stories-of-2014/383935/. [Accessed: 09-Dec-2015]. [51] “2014 Year in Review \| Facebook Newsroom.” [Online]. Available: http://newsroom.fb.com/ news/2014/12/2014-year-in-review/. [Accessed: 09-Dec-2015]. [52] “Facebook’s most talked-about topics of 2014 - CBS News.” [Online]. Available: http://www.cbsnews.com/news/facebooks-most- talked-abouttopics-of-2014/. [Accessed: 09-Dec-2015]. [53] “Twitter’s top tweets and retweets of 2014: Ellen, World Cup score big - TODAY.com.” [Online]. Available: http://www.today.com/money/ twitterstop- tweets-retweets-2014-ellen-world-cup-score-big-1D80349123. [Accessed: 09-Dec-2015]. [54] “Twitter And Facebook Launch Their 2014 ‘Year In Review’ With Top Content, Trends & More.” [Online]. Available: http://marketingland.com/twitter-facebook-launch-2014-year-review-topcontent-trends-110643. [Accessed: 09-Dec-2015]. [55] “Facebook,” 2011. [Online]. Available: http://www.facebook.com/. [56] “Facebook,” 2011. [Online]. Available: http://www.facebook.com/pres/info.php?statistics [57] “Harpsocial,” p. http://www.harpsocial.com/ 2011/04/social–medias–sh, 2011. [58] “Twitter,” p. http://ww.twitter.com/, 2011. [59] N. Diakopoulos, M. Naaman, and F. Kivran-Swaine, “Diamonds in the rough: Social media visual analytics for journalistic inquiry,” VAST 10 - IEEE Conf. Vis. Anal. Sci. Technol. 2010, Proc., pp. 115–122, 2010. [60] B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury, “Twitter power: Tweets as electronic word of mouth,” J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 11, pp. 2169–2188, 2009. [61] A. Shrivastava and B. Pant, “Opinion extraction and classification of real time Facebook Status,” Glob. J. Comput. Sci. Technol., vol. 12, no. 8, 2012. [62] H. Kwak, C. Lee, H. Park, and S. Moon, “What is Twitter, a social network or a news media?,” in Proceedings of the 19th international conference on World wide web, 2010, pp. 591–600. [63] M. R. Morris, J. Teevan, and K. Panovich, “What Do People Ask Their Social Networks, and Why?,” Chi, vol. 69, p. 1739, 2010. [64] A. Kothari, W. Magdy, K. Darwish, A. Mourad, and A. Taei, “Detecting Comments on News Articles in Microblogs.” [65] C. Lin, Y. He, and R. Everson, “Sentence subjectivity detection with weaklysupervised learning,” pp. 1153–1161, 2011. [66] S. Amer-Yahia, S. Anjum, A. Ghenai, A. Siddique, S. Abbar, S. Madden, A. Marcus, and M. El-Haddad, “Maqsa: a system for social analytics on news,” in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 2012, pp. 653–656. [67] R. Feldman and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, 2007. [68] B. Liu, “Sentiment analysis and subjectivity,” Handb. Nat. Lang. Process., vol. 2, pp. 627–666, 2010. [69] B. Liu, “Sentiment Analysis and Opinion Mining,” Synth. Lect. Hum. Lang. Technol., vol. 5, no. 1, pp. 1–167, May 2012. [70] A. Montoyo, P. MartíNez-Barco, and A. Balahur, “Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments,” Decis. Support Syst., vol. 53, no. 4, pp. 675–679, 2012. [71] M. Sadegh, R. Ibrahim, and Z. A. Othman, “Opinion mining and sentiment analysis: A survey,” Int. J. Comput. Technol., vol. 2, no. 3, pp. 171–178, 2012. [72] P. Case and G. D. V, “Opinion Mining and Classification of User Reviews in Social Media,” vol. 7782, pp. 37–41, 2014. [73] M. Tsytsarau and T. Palpanas, “Survey on mining subjective data on the web,” Data Min. Knowl. Discov., vol. 24, no. 3, pp. 478–514, 2012. [74] L.-W. Ku, L.-Y. Lee, T.-H. Wu, and H.-H. Chen, “Major topic detection and its application to opinion summarization,” in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005, pp. 627–628. [75] S.-M. Kim and E. Hovy, “Automatic detection of opinion bearing words and sentences,” in Companion Volume to the Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), 2005, pp. 61–66. [76] A. Balahur, R. Steinberger, M. Kabadjov, V. Zavarella, E. Van Der Goot, M. Halkia, B. Pouliquen, and J. Belyaeva, “Sentiment analysis in the news,” arXiv Prepr. arXiv1309.6202, 2013. [77] G. Vinodhini and R. Chandrasekaran, “Sentiment Analysis and Opinion Mining: A Survey,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 2, no. 6, pp. 282–292, 2012. [78] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014. [79] N. Isa, M. Puteh, R. Mohamad, and H. Raja, “Sentiment Classification of Malay Newspaper Using Immune Network ( SCIN ),” vol. III, 2013. [80] G. Jaganadh, “Opinion mining and Sentiment analysis CSI communication,” 2012. [81] G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. J. Miller, “Introduction to WordNet: An On-line Lexical Database *,” Int. J. Lexicogr., vol. 3, no. 4, pp. 235–244, Jan. 1990. [82] S. Mohammad, C. Dunne, and B. Dorr, “Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus,” in Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2-Volume 2, 2009, pp. 599–608. [83] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” Proc. Conf. Empir. Methods Nat. Lang. Process. July 6-7, 2002, Philadephia, Pennsylvania, USA, pp. 79–86, 2002. [84] H. Tang, S. Tan, and X. Cheng, “A survey on sentiment detection of reviews,” Expert Syst. Appl., vol. 36, no. 7, pp. 10760–10773, 2009. [85] S. Baccianella, A. Esuli, and F. Sebastiani, “SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.,” in LREC, 2010, vol. 10, pp. 2200–2204. [86] D. Das, A. K. Kolya, A. Ekbal, and S. Bandyopadhyay, “Temporal analysis of sentiment events–a visual realization and tracking,” in Computational Linguistics and Intelligent Text Processing, Springer, 2011, pp. 417–428. [87] G. Mishne and M. De Rijke, “MoodViews: Tools for Blog Mood Analysis.,” in AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, 2006, pp. 153–154. [88] G. B. Tran, M. Alrifai, I. A. Intelligence, and N. Language, “Predicting Relevant News Events for Timeline Summaries,” Www, pp. 91–92, 2013. [89] D. Bhattacharya and S. Ram, “Sharing news articles using 140 characters: A diffusion analysis on twitter,” Proc. 2012 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2012, pp. 966–971, 2012. [90] “Top 10 Arabic YouTube Channels in the Middle East \| IstiZada.” [Online]. Available: http://istizada.com/blog/top-10-arabic-youtube-channels/. [Accessed: 09-Dec-2015]. [91] “Al_Jazeera_English,” 2015. [Online]. Available: https://en.wikipedia.org/wiki/ Al_Jazeera_English. [Accessed: 06-Dec-2015]. [92] “Al_Arabiya,” 2015. [Online]. Available: https://en.wikipedia.org/wiki/Al_Arabiya#cite_ note-cablegatesearch1-17. [Accessed: 06-Dec-2015]. [93] “Media Use in the Middle East:An Eight-Nation Survey - NU-Q.” [Online]. Available: http://www.scribd.com/doc/137906439/Media-Use- in-the-Middle-East-An-Eight-Nation-Survey-NU-Q. [Accessed: 09-Dec-2015]. [94] J. Kleinnijenhuis, F. Schultz, D. Oegema, and W. van Atteveldt, “Financial news and market panics in the age of high-frequency sentiment trading algorithms,” Journalism, p. 1464884912468375, 2013. [95] L.-C. Yu, J.-L. Wu, P.-C. Chang, and H.-S. Chu, “Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news,” Knowledge-Based Syst., vol. 41, pp. 89– 97, Mar. 2013. [96] J. Teevan, D. Ramage, and M. R. Morris, “# TwitterSearch: a comparison of microblog search and web search,” in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 35–44. [97] “Billion-Dollar Weather and Climate Disasters: Overview \| National Climatic Data Center (NCDC).” [Online]. Available: http://www.ncdc.noaa.gov/billions/. [Accessed: 03-Apr-2015]. [98] Wikipedia, “Diseases and disorders,” 2015. [Online]. Available: http://en.wikipedia.org/ wiki/Disease. [Accessed: 17-Apr-2015]. [99] Wikipedia, “Terrorism,” 2015. [Online]. Available: http://en.wikipedia.org/wiki/ Terrorism. [Accessed: 19-Apr-2015]. [100] Ask.com, “what are the effects of terrorist attacks,” 2015. [Online]. Available: http://www.ask.com/. [Accessed: 01-Jan-2015]. [101] Wikipedia, “Conflict_(process),” 2014. [Online]. Available: http://en.wikipedia.org/ wiki/Conflict_(process). [Accessed: 22-Dec-2014]. [102] Collinsdictionary, “plane-crash,” 2015. [Online]. Available: http://www.collinsdictionary.com/dictionary/ english/plane-crash. [Accessed: 01-Jan-2015]. [103] C. Strapparava and R. Mihalcea, “Learning to identify emotions in text,” in Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 1556–1560. [104] R. Plutchik, “A general psychoevolutionary theory of emotion,” Theor. Emot., vol. 1, 1980. [105] “List of Human Emotions - List of Human Emotions.” [Online]. Available: http://www.listofhumanemotions.com/listof humanemotions. [Accessed: 21-May-2015]. [106] J. Allan, R. Papka, and V. Lavrenko, “On-line new event detection and tracking,” in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, 1998, pp. 37–45. [107] A. Kontostathis, L. M. Galitsky, W. M. Pottenger, S. Roy, and D. J. Phelps, “A survey of emerging trend detection in textual data mining,” in Survey of Text Mining, Springer, 2004, pp. 185–224. [108] “Poll results: Top languages for analytics/data mining programming.” [Online]. Available: http://www.kdnuggets.com/2012/08/ poll-analytics-datamining-programming- languages.html. [Accessed: 23-Dec-2015]. [109] “RapidMiner at CeBIT 2010: the Enterprise Edition, Rapid-I and Cloud Mining - Data Mining - Blog.com.” [Online]. Available: http://www.datamining-blog.com/cloud-mining/ rapidminer-cebit-2010/. [Accessed: 09-Dec- 2015]. [110] “RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series): Markus Hofmann, Ralf Klinkenberg: 9781482205497: Amazon.com: Books.” [Online]. Available: http://www.amazon.com/RapidMiner-Analytics-Applications-Knowledge-Discovery/dp/ 1482205491. [Accessed: 09-Dec-2015]. [111] “KDnuggets Annual Software Poll: RapidMiner and R vie for first place.” [Online]. Available: http://www.kdnuggets.com/ 2013/06/kdnuggets-annualsoftware-poll-rapidminer -r-vie-for-first-place.html. [Accessed: 09-Dec-2015]. [112] “Rexer Analytics 5th Annual Data Miner Survey - 2011.” [Online]. Available: http://www.rexeranalytics.com/Data-Miner-Survey-Results-2011.html. [Accessed: 09-Dec-2015]. [113] “German Predictive Analytics Startup Rapid-I Rebrands As RapidMiner, Takes $5M From Open Ocean, Earlybird To Tackle The U.S. Market.” [Online]. Available: http://techcrunch.com/2013/11/04/german-predictiveanalytics-startup-rapid-i-rebrands-as-rapidminer-takes-5m-from-open-oceanearlybird- to-tackle-the-u-s-market/. [Accessed: 09-Dec-2015]. [114] F. Ben Abdesslem, I. Parris, and T. Henderson, “Reliable online social network data collection,” in Computational Social Networks, Springer, 2012, pp. 183–210. [115] B. Rieder, “Studying Facebook via data extraction: the Netvizz application,” in Proceedings of the 5th Annual ACM Web Science Conference, 2013, pp. 346–355. [116] C. Troussas, M. Virvou, K. Junshean Espinosa, K. Llaguno, and J. Caro, “Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning,” in Information, Intelligence, Systems and Applications (IISA), 2013 Fourth International Conference on, 2013, pp. 1–6. [117] A. Shrivatava and B. Pant, “Opinion Extraction and Classification of Real Time Facebook Status,” vol. 12, no. 8, 2012. [118] R. Rogers, “The end of the virtual,” 2009. [119] C. Cesarano, B. Dorr, A. Picariello, D. Reforgiato, A. Sagoff, and V. Subrahmanian, “Oasys: An opinion analysis system,” in AAAI spring symposium on computational approaches to analyzing weblogs, 2004. [120] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manag., vol. 24, no. 5, pp. 513–523, 1988. [121] “TheySay PreCeive API Demo.” [Online]. Available: http://apidemo.theysay.io/. [Accessed: 10-Dec-2015]. [122] “Python NLTK Sentiment Analysis with Text Classification Demo.” [Online]. Available: http://text-processing.com/demo/sentiment/. [Accessed: 10-Dec-2015]. [123] “DepecheMood - Try Our Online Demo!” [Online]. Available: http://www.depechemood.eu/DepecheMood.html. [Accessed: 10-Dec-2015]. [124] C. Strapparava and R. Mihalcea, “Semeval-2007 task 14: Affective text,” in Proceedings of the 4th International Workshop on Semantic Evaluations, 2007, pp. 70–74. [125] E. Haddi, X. Liu, and Y. Shi, “The role of text pre-processing in sentiment analysis,” Procedia Comput. Sci., vol. 17, pp. 26–32, 2013. [126] D. Meyer, K. Hornik, and I. Feinerer, “Text mining infrastructure in R,” J. Stat. Softw., vol. 25, no. 5, pp. 1–54, 2008. [127] M. F. Porter, “An algorithm for suffix stripping,” Program, vol. 14, no. 3, pp. 130–137, 1980. [128] R. Feldman, “Techniques and applications for sentiment analysis,” Commun. ACM, vol. 56, no. 4, pp. 82–89, 2013. [129] F. Sebastiani, “Machine learning in automated text categorization,” ACM Comput. Surv., vol. 34, no. 1, pp. 1–47, 2002. [130] Y. Mejova and P. Srinivasan, “Exploring Feature Definition and Selection for Sentiment Classifiers,” pp. 546–549, 2011. [131] S. Li and C. Zong, “A new approach to feature selection for text categorization,” in Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE’05. Proceedings of 2005 IEEE International Conference on, 2005, pp. 626–630. [132] Z. Zheng, X. Wu, and R. Srihari, “Feature selection for text categorization on imbalanced data,” ACM Sigkdd Explor. Newsl., vol. 6, no. 1, pp. 80–89, 2004. [133] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267–307, 2011. [134] J. S. Kessler and N. Nicolov, “Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations.,” in ICWSM, 2009. [135] J. Kleinberg, “Bursty and hierarchical structure in streams,” Data Min. Knowl. Discov., vol. 7.4, pp. 373–397. [136] K. Balog, G. Mishne, and M. De Rijke, “Why are they excited?: identifying and explaining spikes in blog mood levels,” in Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations, 2006, pp. 207–210. [137] L. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257–286, 1989. [138] D. Maynard and A. Funk, “Automatic detection of political opinions in Tweets,” pp. 81–92. [139] B. Ohana and B. Tierney, “Sentiment classification of reviews using SentiWordNet,” in 9th. IT & T Conference, 2009, p. 13. [140] P. Chaovalit and L. Zhou, “Movie review mining: A comparison between supervised and unsupervised classification approaches,” in System Sciences, 2005. HICSS’05. Proceedings of the 38th Annual Hawaii International Conference on, 2005, p. 112c–112c. [141] T. Joachims, Text categorization with support vector machines: Learning with many relevant features. Springer, 1998. [142] K. T. Durant and M. D. Smith, “Mining sentiment classification from political web logs,” in Proceedings of Workshop on Web Mining and Web Usage Analysis of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (WebKDD-2006), Philadelphia, PA, 2006. [143] A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Proj. Report, Stanford, pp. 1–12, 2009. [144] R. Zheng, J. Li, H. Chen, and Z. Huang, “A framework for authorship identification of online messages: Writing‐ style features and classification techniques,” J. Am. Soc. Inf. Sci. Technol., vol. 57, no. 3, pp. 378–393, 2006. [145] Q. Ye, Z. Zhang, and R. Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches,” Expert Syst. Appl., vol. 36, no. 3, pp. 6527–6535, 2009. [146] H. Cui, V. Mittal, and M. Datar, “Comparative experiments on sentiment classification for online product reviews,” in AAAI, 2006, vol. 6, pp. 1265–1270. [147] E. Airoldi, X. Bai, and R. Padman, “Markov blankets and meta-heuristics search: Sentiment extraction from unstructured texts,” in Advances in Web Mining and Web Usage Analysis, Springer, 2006, pp. 167–187. [148] B. Xu, T.-J. Zhao, D.-Q. Zheng, and S.-Y. Wang, “Product features mining based on conditional random fields model,” in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, 2010, vol. 6, pp. 3353–3357. [149] D. K. Kirange and R. R. Deshmukh, “Emotion Classification of News Headlines Using Svm,” vol. 5, pp. 104–106, 2012. [150] N. V Chawla, N. Japkowicz, and A. Kotcz, “Editorial: special issue on learning from imbalanced data sets,” ACM Sigkdd Explor. Newsl., vol. 6, no. 1, pp. 1–6, 2004. [151] Y. Y. Yang, M. Mahfouf, G. Panoutsos, Q. Zhang, and S. Thornton, “Adaptive neural-fuzzy inference system for classification of rail quality data with bootstrapping-based over-sampling,” in Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, 2011, pp. 2205–2212. [152] G. Forman, “An extensive empirical study of feature selection metrics for text classification,” J. Mach. Learn. Res., vol. 3, pp. 1289–1305, 2003. [153] D. Mladenic and M. Grobelnik, “Feature selection for unbalanced class distribution and naive bayes,” in ICML, 1999, vol. 99, pp. 258–267. [154] G. E. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM Sigkdd Explor. Newsl., vol. 6, no. 1, pp. 20–29, 2004. [155] M. Porter, “The Porter stemming algorithm, 2005,” See http//www. tartarus.org/~ martin/PorterStemmer. [156] W. B. Frakes, “Information Retrieval: CHAPTER 8: STEMMING ALGORITHMS.” [Online]. Available: http://dns.uls.cl/~ej/daa_08/ Algoritmos/books/book5/chap08.htm. [Accessed: 17-Dec-2015].

A study of feature exraction techniques for classifying topics and sentiments from news posts

Similar Items