Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Singla, Amit
- Intensified Multidimensional Style for User Belief Mining from Social Media
Abstract Views :133 |
PDF Views:0
Authors
Affiliations
1 J.R.N. Rajasthan Vidyapeeth, Udaipur, IN
2 Govt. Engineering College, Bikaner, IN
3 VC, J.R.N. Rajasthan Vidyapeeth, Udaipur, IN
1 J.R.N. Rajasthan Vidyapeeth, Udaipur, IN
2 Govt. Engineering College, Bikaner, IN
3 VC, J.R.N. Rajasthan Vidyapeeth, Udaipur, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 26 (2017), Pagination: 244-251Abstract
Big data analytics is used to examine large sets of data which may contain diversity of different types of data, it can be used to decrypt cryptic symbiology, correlating previously not known variables, finding the trends in the market, checking preferences of customers and finding out data about various businesses and institutions. The re-sult can be used to conduct informative market strategizing, checking out chances to generate higher income, to pro-vide effective consumer-oriented services, to improve effectiveness of operations and to provide competition-edge over competitors and other institutional profits. The main aim of Big data Analysis is to aid in better and informative decision making for the firms by taking advantage of capable data-scientists, genius model makers as well as other trained scientists to verify chunks of information that may be unused by the traditional programs. It may include ana-lyzing special log and internet based information, internet network data and social-analysis of reports. It can also be used to analyze network records, caller details and other information gathered and operated by IOT devices. It can be used to bind with big data and unstructured data as well as partially structured data.Keywords
Big Data Analytics, Emotions Mining, Social Media Analytics, User Belief Mining.Full Text
References
- Awrahman, B., & Alatas, B. (2017). Sentiment Analysis and Opinion Mining within Social Networks using Kons-tanz Information Miner. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 15-22.
- Hürlimann, M., Davis, B., Cortis, K., Freitas, A., Handschuh, S., & Fernández, S. (2016, September). A Twitter Sentiment Gold Standard for the Brexit Referendum. In Proceedings of the 12th International Conference on Se-mantic Systems (pp. 193-196). ACM.
- Kotwal, A., Fulari, P., Jadhav, D., & Kad, R. (2016). Improvement in Sentiment Analysis of Twitter Data Using Hadoop. Imperial Journal of Interdisciplinary Research, 2(7).
- Cambria, E., Fu, J., Bisio, F., & Poria, S. (2015, January). AffectiveSpace 2: Enabling Affective Intuition for Con-cept-Level Sentiment Analysis. In AAAI (pp. 508-514).
- Martínez-Cámara, E., Martín-Valdivia, M. T., Urena-López, L. A., & Montejo-Ráez, A. R. (2014). Sentiment anal-ysis in Twitter. Natural Language Engineering, 20(01), 1-28.
- Abdul-Mageed, M., Diab, M., & Kübler, S. (2014). SAMAR: Subjectivity and sentiment analysis for Arabic social media. Computer Speech & Language, 28(1), 20-37.
- Cambria, E., Schuller, B., Liu, B., Wang, H., & Havasi, C. (2013). Knowledge-based approaches to concept-level sentiment analysis. IEEE Intelligent Systems, 28(2), 12-14.
- Shahheidari, S., Dong, H., & Daud, M. N. R. B. (2013, July). Twitter sentiment mining: A multi domain analysis. In Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference on (pp. 144-149). IEEE.
- Saif, H., He, Y., & Alani, H. (2012, November). Semantic sentiment analysis of twitter. In International Semantic Web Conference (pp. 508-524). Springer Berlin Heidelberg.
- Leong, C. K., Lee, Y. H., & Mak, W. K. (2012). Mining sentiments in SMS texts for teaching evaluation. Expert Systems with Applications, 39(3), 2584-2589.
- Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., & Li, P. (2011, August). 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.
- Asur, S., & Huberman, B. A. (2010, August). 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.
- Bifet, A., & Frank, E. (2010, October). Sentiment knowledge discovery in twitter streaming data. In International Conference on Discovery Science (pp. 1-15). Springer Berlin Heidelberg.
- Bollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM, 11, 450-453.
- Awrahman, B., & Alatas, B. (2017). Sentiment Analysis and Opinion Mining within Social Networks using Kons-tanz Information Miner. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 15-22.
- Zhao, J., & Gui, X. (2017). Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analy-sis. IEEE