원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.1
2016.01
pp.77-86
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Twitter is an online social networking site which contains rich amount of data that can be a structured, semi-structured and un-structured data. In this work, a method which performs classification of tweet sentiment in Twitter is discussed. To improve its scalability and efficiency, it is proposed to implement the work on Hadoop Ecosystem, a widely-adopted distributed processing platform using the Map Reduce parallel processing paradigm. Finally, extensive experiments will be conducted on real-world data sets, with an expectation to achieve comparable or greater accuracy than the proposed techniques in literature.
목차
Abstract
1. Introduction
2. Problem Definition
3. Literature Review
3.1 Lin, Jimmy, and Alek Kolcz. "Large-Scale Machine Learning at Twitter." In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 793-804. ACM, 2012.
3.2 Bian, Jiang, Umit Topaloglu, and Fan Yu. "Towards Large-Scale Twitter Mining for Drug-Related Adverse Events" In Proceedings of the 2012 international workshop on Smart health and wellbeing, pp. 25-32. ACM, 2012.
3.3 Liu, Bingwei, Erik Blasch, Yu Chen, Dan Shen, and Genshe Chen. "Scalable Sentiment Classification for Big Data Analysis Using Naive Bayes Classifier" In Big Data, 2013 IEEE International Conference on, pp. 99-104. IEEE, 2013.
3.4 ÁlvaroCuesta, David F., and María D. R-Moreno. "A Framework for Massive Twitter Data Extraction and Analysis", In Malaysian Journal of Computer Science, pp 50-67 (2014):1.
3.5 Skuza, Michal, and Andrzej Romanowski. "Sentiment analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction" In Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on, pp. 1349-1354. IEEE, 2015.
3.6 Tare, Mohit, Indrajit Gohokar, Jayant Sable, Devendra Paratwar, and Rakhi Wajgi. "Multi-Class Tweet Categorization Using Map Reduce Paradigm" In International Journal of Computer Trends and Technology. pp 78 - 81 (2014)
3.7 Comparitive Analysis
4. Development Environment
5. Development Methodology
5.1 Data Streaming
5.2. Preprocessing
5.3 Sentiment Polarity Analysis
5.4 Visualization
6. Evaluation Metrics
7. Conclusion
References
1. Introduction
2. Problem Definition
3. Literature Review
3.1 Lin, Jimmy, and Alek Kolcz. "Large-Scale Machine Learning at Twitter." In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 793-804. ACM, 2012.
3.2 Bian, Jiang, Umit Topaloglu, and Fan Yu. "Towards Large-Scale Twitter Mining for Drug-Related Adverse Events" In Proceedings of the 2012 international workshop on Smart health and wellbeing, pp. 25-32. ACM, 2012.
3.3 Liu, Bingwei, Erik Blasch, Yu Chen, Dan Shen, and Genshe Chen. "Scalable Sentiment Classification for Big Data Analysis Using Naive Bayes Classifier" In Big Data, 2013 IEEE International Conference on, pp. 99-104. IEEE, 2013.
3.4 ÁlvaroCuesta, David F., and María D. R-Moreno. "A Framework for Massive Twitter Data Extraction and Analysis", In Malaysian Journal of Computer Science, pp 50-67 (2014):1.
3.5 Skuza, Michal, and Andrzej Romanowski. "Sentiment analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction" In Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on, pp. 1349-1354. IEEE, 2015.
3.6 Tare, Mohit, Indrajit Gohokar, Jayant Sable, Devendra Paratwar, and Rakhi Wajgi. "Multi-Class Tweet Categorization Using Map Reduce Paradigm" In International Journal of Computer Trends and Technology. pp 78 - 81 (2014)
3.7 Comparitive Analysis
4. Development Environment
5. Development Methodology
5.1 Data Streaming
5.2. Preprocessing
5.3 Sentiment Polarity Analysis
5.4 Visualization
6. Evaluation Metrics
7. Conclusion
References
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