The week's pick
Random Articles
Reseach Article
Brand Analysis using Named Entity Recognition and Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 45 |
| Year of Publication: 2018 |
| Authors: Toshal Patel, Megha Gupta, A. J. Agrawal |
10.5120/ijca2018917139
|
Toshal Patel, Megha Gupta, A. J. Agrawal . Brand Analysis using Named Entity Recognition and Sentiment Analysis. International Journal of Computer Applications. 179, 45 ( May 2018), 1-5. DOI=10.5120/ijca2018917139
Abstract
Internet has become a platform to host a myriad of services. An individual can make use of any services or resources and share his or her views about it at the same time. Today, social media platforms have become the largest source of accessing global reviews of the public regarding various movies, products, songs, etc. This paper focuses on proposing a method for gathering and analyzing the reviews of the people with respect to a company or different products of the company, and generating a report that will give a sentiment analysis of the reviews of the company’s customers. In this paper, we discuss approaches to extract data from Twitter for a particular company or product, and performing Named Entity Recognition to extracted the related tweets. Analysis of the tweets will help in segregating the dataset based on their sentiments, generating a report of positive, negative or neutral customer reviews of a company’s products or the brand itself.
References
- R. Bhayani A. Go and L. Huang. Twitter sentiment classification using distant supervision. CS224N, Project Report, Stanford University, Stanford, CA, 2009.
- H. Gabelica A. Mihanovic and Z. Krsti. Big data and sentiment analysis using knime: Online reviews vs. social media, 2004. MIPRO.
- Mausam A. Ritter, S. Clark and O. Etzioni. Named entity recognition in tweets: an experimental study. In EMNLP11 Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1524–1534, 2011.
- K. Sobel B. J. Jansen, M. Zhang and A. Chowdury. Microblogging as online word of mouth branding. In CHI EA 09: Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, pages 3859–3864, 2009. ACM.
- L. Bing and K. C. C. Chan. A fuzzy logic approach for opinion mining on large scale twitter data. In UCC 14 Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pages 652–657, December 2014.
- A. McCallum J. D. Lafferty and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labelling sequence data. In ICML’01 roceedings of the Eighteenth International Conference on Machine Learning, pages 282– 289. Morgan Kaufmann Publishers, 2001.
- R. Janardhana. How to build a twitter sentiment analyzer, May 2012. https://www.ravikiranj.net/posts/2012/code/how-buildtwitter- sentiment-analyzer/.
- B. Pang and L. Lee. Opinion Mining and Sentiment Analysis, volume 2, pages 1–135. 2008. http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf, unpublished.
- F. Morstatter S. Kumar and H. Liu. Twitter Data Analytics, pages 21–51. Cambridge University Press, Jan 2015.
- H. M. Zin N. M. Sharef and S. Nadali. Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data, volume 12, pages 153–168. 2016.
- R. Singh and R. Kaur. Sentiment analysis on social media and online review. International Journal of Computer Applications (0975-8887), 121(20):44–48, July 2015.
- J. Wiebe T. Wilson and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of Human Language Technologies Conference/Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), 2005.
Index Terms
Keywords