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Missing Value Treatment using Effective Optimization on Data from Multiple Social Media


Affiliations
1 Department of Computer Science, Punjabi University, Patiala, Punjab, India
2 University Computer Center, Punjabi University, Patiala, Punjab, India
 

Missing value are broad in numerous genuine applications. Missing value imputation and in addition treatment is vital on the grounds that the skipping of missing value based records can harm the general results. For instance, if the client conclusions about information leak in India are fetched from social media then the client having hidden personal information can be covered in missing records. Such records cannot be skipped because of the privacy concerns of the users and therefore missing value imputation should be implemented on such records. In this research work, random forest approach for missing value imputation is devised and implemented on the different types of social media like youtube, twitter, tumblr.

Keywords

Social Media, Random Forest Approach, Missing Value, Missing Value Imputation.
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  • Bifet, A., & Frank, E.. Sentiment knowledge discovery in twitter streaming data. In International Conference on Discovery Science. Springer Berlin Heidelberg, 2010

  • Bollen, J., Mao, H., & Pepe, A.. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM, 11, 450-453, 2009

  • Bollen, J., Mao, H., & Pepe, A.. Determining the Public Mood State by Analysis of Microblogging Posts. In ALIFE (pp. 667-668), 2010

  • Asur, S., & Huberman, B. A.. Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE, 2010

  • Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., & Li, P.. User-level sentiment analysis incorporating social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1397-1405). ACM, 2011

  • Saif, H., He, Y., & Alani, H.. Semantic sentiment analysis of twitter. In International Semantic Web Conference (pp. 508-524). Springer Berlin Heidelberg, 2012

  • Leong, C. K., Lee, Y. H., & Mak, W. K.. Mining sentiments in SMS texts for teaching evaluation. Expert Systems with Applications, 39(3), 2584-2589, 2012

  • Wang, H., Cambria, E., Schuller, B., Liu, B., & Havasi, C.. Knowledge-based approaches to concept-level sentiment analysis. IEEE Intelligent Systems, 28(2), 12-14, 2013

  • Dong, H., Shahheidari, S., & Daud, M. N. R. B.. Twitter sentiment mining: A multi domain analysis. In Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference (pp. 144-149). IEEE, 2013

  • Cambria, E., Fu, J., Bisio, F., & Poria, S.. AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis. In AAAI (pp. 508-514), 2013

  • Kotwal, A., Fulari, P., Jadhav, D., & Kad, R.. Improvement in Sentiment Analysis of Twitter Data Using Hadoop. Imperial Journal of Interdisciplinary Research, 2(7), 2014

  • Poria, S. Cambria, E., Fu, J., Bisio, F., &. AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis. In AAAI (pp. 508-514), 2015

  • Wehrmann J, Becker W, Cagnini HE, Barros RC. A character-based convolutional neural network for language-agnostic Twitter sentiment analysis. InNeural Networks (IJCNN), 2017 International Joint Conference on 2017 May 14 (pp. 2384-2391). IEEE,2017

  • Al-Rubaiee H, Qiu R, Li D. Identifying Mubasher software products through sentiment analysis of Arabic tweets. Industrial Informatics and Computer Systems (CIICS), 2016 International Conference on 2016 Mar 13 (pp. 1-6). IEEE, 2016

  • Heredia B, Khoshgoftaar TM, Prusa J, Crawford M. Cross-domain sentiment analysis: An empirical investigation. Information Reuse and Integration (IRI), 2016 IEEE 17th International Conference on 2016 Jul 28 (pp. 160-165). IEEE, 2016

  • Blaz CC, Becker K. Sentiment analysis in tickets for it support. InMining Software Repositories (MSR), 2016 IEEE/ACM 13th Working Conference on 2016 May 14 (pp. 235-246). IEEE, 2016

  • Fiarni C, Maharani H, Pratama R. Sentiment analysis system for Indonesia online retail shop review using hierarchy Naive Bayes technique. InInformation and Communication Technology (ICoICT), 2016 4th International Conference on 2016 May 25 (pp. 1-6). IEEE, 2016

  • Pamungkas EW, Putri DG. An experimental study of lexicon-based sentiment analysis on Bahasa Indonesia. Engineering Seminar (InAES), International Annual 2016 Aug 1 (pp. 28-31). IEEE, 2016

  • Nithya R, Maheswari D. Correlation of feature score to overall sentiment score for identifying the promising features. Computer Communication and Informatics (ICCCI), 2016 International Conference on 2016 Jan 7 (pp. 1-5). IEEE, 2016

  • Bouazizi M, Ohtsuki TO. A pattern-Based approach for Sarcasm Detection on Twitter. IEEE Access. 2016;4:5477-88, 2016

  • Gatti L, Guerini M, Turchi M. Sentiwords: Deriving a high precision and high coverage lexicon for sentiment analysis. IEEE Transactions on Affective Computing. 2016 Oct 1;7(4):409-21, 2016

  • Biltawi M, Etaiwi W, Tedmori S, Hudaib A, Awajan A. Sentiment classification techniques for Arabic language: A survey. Information and Communication Systems (ICICS), 2016 7th International Conference on 2016 Apr 5 (pp. 339-346). IEEE, 2016

  • Rabab'ah AM, Al-Ayyoub M, Jararweh Y, Al-Kabi MN. Evaluating sentistrength for arabic sentiment analysis. Computer Science and Information Technology (CSIT), 2016 7th International Conference on 2016 Jul 13 (pp. 1-6). IEEE, 2016

  • Barve, Abhishek, Manali Rahate, Ayesha Gaikwad, and Priyanka Patil. "Terror Attack Identifier: Classify using KNN, SVM, Random Forest algorithm and alert through messages." International Research Journal of Engineering and Technology (IRJET) 2018

  • Estee, Jan. “Using Machine Learning to Detect Fake Identities: Bots vs Humans”. IEEE Access. Volume 6 2018

  • Fengfeng Fan, Zhanhuai Li and Yanyan Wang, “On-Line Imputation for Missing Values”, 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics CISP-BMEI 2017

  • Bichen Shi, Gevorg Poghosyan, Georgiana Ifrim, and Neil Hurley, “Hashtagger+: Efficient High-Coverage Social Tagging of Streaming News”, IEEE Transactions on Knowledge and Data Engineering, 2018

  • Lidong Wang,Randy Jones, ”Big Data Analytics for Disparate Data”, American journal of Intelligent System, 2017.


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  • Missing Value Treatment using Effective Optimization on Data from Multiple Social Media

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Authors

Sukhman Kaur
Department of Computer Science, Punjabi University, Patiala, Punjab, India
Neeraj Sharma
Department of Computer Science, Punjabi University, Patiala, Punjab, India
Kawaljeet Singh
University Computer Center, Punjabi University, Patiala, Punjab, India

Abstract


Missing value are broad in numerous genuine applications. Missing value imputation and in addition treatment is vital on the grounds that the skipping of missing value based records can harm the general results. For instance, if the client conclusions about information leak in India are fetched from social media then the client having hidden personal information can be covered in missing records. Such records cannot be skipped because of the privacy concerns of the users and therefore missing value imputation should be implemented on such records. In this research work, random forest approach for missing value imputation is devised and implemented on the different types of social media like youtube, twitter, tumblr.

Keywords


Social Media, Random Forest Approach, Missing Value, Missing Value Imputation.

References