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Various Mechanisms for understanding Short Text

Pournima G. Kamble, S. B. Bhagate ,

Affiliations
Dept. of Computer Science and Engineering, DKTE’s TEI, Ichalkaranji (An Autonomous Institute), 416115, India.
:10.22362/ijcert/2017/v4/i11/xxxx [UNDER PROCESS]


Abstract
Now a day’s all people use short text in real life for communication and chatting purpose. Short texts are also uses in news titles, social posts, tweets, conversations, keywords, search queries. Short text understanding is an ambiguous process in opinions, deals, events and private messages. The short text is produce that contain social posts, conversations, keywords and news titles which are limited context and represent the insufficient information or meaning of the text. As short text has more than one meaning, they are difficult to understand as they are ambiguous and noisy. The term can be either single or multi-word. Short texts do not contain sufficient data. Some short texts have unique characteristics. So these short texts are difficult to handle. It required better understand the short text. Semantic analysis is essential to understand the short text accurately. Tasks such as segmentation, part-of-speech tagging, and concept labeling are used for semantic analysis. Conduct short text uses in real life data. The prototype system is built and used to understand the short text. These systems provide the semantic knowledge from knowledge base and collection of written words that are automatically harvest. Creating construction of co-occurrence network showing to better understand for short text.


Citation
Pournima G. Kamble and S. B. Bhagate (2017). Various Mechanisms for understanding Short Text. International Journal of Computer Engineering In Research Trends, 4(11), 519-523. Retrieved from http://ijcert.org/ems/ijcert_papers/V4I1109.pdf


Keywords : Short Text, Part of speech tagger, Semantics, text segmentation, Term Extraction

References
[1] Xiaojiang Lei, Xueming Qian, Member, IEEE, and Guoshuai Zhao, “Rating Prediction based on Social Sentiment from Textual Review”, IEEE Trans. VOL. 18, NO. 9, 2016.
[2] K. H. L. Tso-Sutter, L. B. Marinho, L. Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms”, in Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 1995-1999.
[3] X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle”, IEEE Trans. Knowledge and data engineering. 2014, pp. 1763-1777.
[4] X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks”, in Proc. 18th ACM
SIGKDD Int. Conf. KDD, New York, NY, USA, Aug. 2012, pp. 1267–1275.
[5] M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang, “Social contextual recommendation”, in proc. 21st ACM Int. CIKM, 2012, pp. 45-54.
[6] H. Feng, and X. Qian, “Recommendation via user‟s personality and social contextual”, in Proc. 22nd ACM international conference on information & knowledge management. 2013, pp. 1521-1524.
[7] F. Li, N. Liu, H. Jin, K. Zhao, Q. Yang, X. Zhu, “Incorporating reviewer and product information for review rating prediction,” in Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, 2011, pp. 1820-1825.
[8] G. Ganu, N. Elhadad, A Marian, “Beyond the stars: Improving rating predictions using Review text content”, in 12th International Workshop on the Web and Databases (WebDB 2009). pp. 1-6.
[9] Y. Ren, J. Shen, J. Wang, J. Han, and S. Lee, “Mutual Verifiable Provable Data Auditing in Public Cloud Storage”, Journal of Internet Technology, vol. 16, no. 2, 2015, pp. 317-323.
[10] Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, S. Ma, “Explicit factor models for explainable recommendation based on phrase-level sentiment analysis”, in proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014.
[11] X. Lei, and X. Qian, “Rating prediction via exploring service reputation”, 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), Oct 19-21, 2015, Xiamen, China. pp.1-6.
[12] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms”, in Proc. 10th International Conference on World Wide Web, 2001, pp. 285-295.
[13] K. H. L. Tso-Sutter, L. B.Marinho, L. Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms”, in Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 1995-1999.
[14] R. Salakhutdinov, and A. Mnih, “Probabilistic matrix factorization”, in NIPS, 2008.
[15] X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle”, IEEE Trans. Knowledge and data engineering. 2014, pp. 1763-1777.
[16] L. Qu, G. Ifrim, G. Weikum, “The bag-of-opinions method for review rating prediction from sparse text patterns”, in Proc. 23rd International Conference on Computational Linguistics, 2010, pp. 913–921.
[17] K. Zhang, Y. Cheng, W. Liao, A. Choudhary, “Mining millions of reviews: a technique to rank products based on importance of reviews”, in Proceedings of the 13th International Conference on Electronic Commerce, Aug. 2011, pp. 1-8.
[18] B. Pang, Bo, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques”, in Proc. EMNLP, 2002, pp. 79-86.
[19] D. Tang, Q. Bing, T. Liu, “Learning semantic representations of users and products for document level sentiment classification”, in Proc. 53th Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, July 26-31, 2015, pp. 1014–1023.
[20] T. Nakagawa, K. Inui, and S. Kurohashi, “Dependency tree-based sentiment classification using CRFs with
Hidden Variables”, NAACL, 2010, pp.786-794.
[21] Sunil B. Mane, Kruti Assar, Priyanka Sawant, & Monika Shinde,” Product Rating using Opinion Mining” International Journal of Computer Engineering In Research Trends., vol.4, no.5, pp. 161-168, 2017.
[22] K.Arun A.Srinagesh and M.Ramesh, ”Twitter Sentiment Analysis on Demonetization tweets in India Using R language. “International Journal of Computer Engineering in Research Trends., vol.4, no.6, pp. 252- 258, 2017.
[23] TekurVijetha, M.SriLakshmi and Dr.S.PremKumar,” Survey on Collaborative Filtering and content-Based Recommending. “International Journal of Computer Engineering in Research Trends., vol.2, no.9, pp. 594- 599, 2015.
[24] N.Satish Kumar, Sujan Babu Vadde, ” Typicality Based Content-Boosted Collaborative Filtering Recommendation Framework. “International Journal of Computer Engineering in Research Trends., vol.2, no.11, pp. 809-813, 2015.
[25] D.Ramanjaneyulu,U.Usha Rani,”In Service Oriented MSN Providing Trustworthy Service Evaluation. “International Journal of Computer Engineering in Research Trends., vol.2, no.12, pp. 1192-1197, 2015.


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