The week's pick
Reseach Article
Named Entity Recognition and Aspect based Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 46 |
| Year of Publication: 2019 |
| Authors: Sangeeta Oswal, Ravikumar Soni, Omkar Narvekar, Abhijit Pradha |
10.5120/ijca2019919367
|
Sangeeta Oswal, Ravikumar Soni, Omkar Narvekar, Abhijit Pradha . Named Entity Recognition and Aspect based Sentiment Analysis. International Journal of Computer Applications. 178, 46 ( Sep 2019), 18-23. DOI=10.5120/ijca2019919367
Abstract
Twitter is a microblogging website where people can share their feelings quickly and spontaneously by sending a tweets limited by 280 characters. You can directly address a tweet to someone by adding the target sign “@” or participate to a topic by adding an hashtag “#” to your tweet. Here specific hashtag (#) based tweets are downloaded using tweepy and they are cleansed for removal of irrelevant data then Entity Recognition is performed using the NER Algorithm which specifies different entities belonging to that tweet eg person, place, organization, etc. and finally sentiment analysis is performed where we analyze the general sentiment that can either be positive, negative or neutral at the entity level.
References
- Alan Ritter, Sam Clark, Mausam and Oren Etzioni “Named Entity Recognition in Tweets: An Experimental Study” Proceeding EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing Pages 1524-1534.
- D. Albanese, R. Visintainer, S. Merler, S. Riccadonna, G. Jurman, and C. Furlanello. mlpy: Machine learning Python. CoRR, abs/1202.6548, 2012.
- Perkins, Jacob. Python Text Processing with NLTK 2.0 Cookbook: over 80 Practical Recipes for Using Python's NLTK Suite of Libraries to Maximize Your Natural Language Processing Capabilities. PACKT Publishing, 2010.
- K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” Knowledge-Based Syst., vol. 89, pp. 14–46, 2015.
- Kenneth R. Beesley and Lauri Karttunen. 2002. Finite-State Morphology: Xerox Tools and Tech-niques. Studies in Natural Language Processing.Cambridge University Press.
- L. Zhang and B. Liu, “Aspect and Entity Extraction for Opinion Mining,” pp. 1–40, 2014.
- A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” In LREc, vol. 10, May. 2010, pp. 1320-1326.
- A. K. Jose, N. Bhatia, and S. Krishna, “TwitterSentimentAnalysis”. NationalInstituteof TechnologyCalicut,2010.
- P. Lai, “ExtractingStrongSentimentTrendfromTwitter”. Stanford University, 2012.
- E. Kouloumpis, T. Wilson, and J. Moore, “Twitter Sentiment Analysis:The Good the Bad and theOMG!”, (Vol.5). International AAAI, 2011.
- J. Spencer and G. Uchyigit, “Sentiment or: Sentiment Analysis of Twitter Data,” Second Joint Conference on Lexicon and Computational Semantics. Brighton:University of Brighton, 2008.
- P. Nakov, Z. Kozareva, A. Ritter, S. Rosenthal, V. Stoyanov, T. Wilson, Sem Eval-2013 Task2:Sentiment AnalysisinTwitter (Vol.2,pp. 312-320 2013.
- X. Zheng, Z. Lin, X. Wang, K.-J. Lin, and M. Song, “Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification,” Knowledge-Based Syst., vol. 61, pp. 29–47, May 2014.
- T. C. Chinsha and S. Joseph, “A syntactic approach for aspect based opinion mining,” in Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015, 2015, pp. 24–31.
- S. Poria, E. Cambria, L.-W. Ku, C. Gui, and A. Gelbukh, “A Rule-Based Approach to Aspect Extraction from Product Reviews,” Work. Nat. Lang. Process. Soc. Media, pp. 28–37, 2014.
- A. K. Jose, N. Bhatia, and S. Krishna, “TwitterSentiment Analysis”.NationalInstituteof TechnologyCalicut,2010.
- D. Boyd, S. Golder, & G. Lotan, “Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter,” System Sciences (HICSS), 2010 …. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5428313
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