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Enhanced Sentiment Analysis by using Social Media
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 15 |
| Year of Publication: 2018 |
| Authors: Shubhangi D. C., Priyadarshini M. |
10.5120/ijca2018917832
|
Shubhangi D. C., Priyadarshini M. . Enhanced Sentiment Analysis by using Social Media. International Journal of Computer Applications. 182, 15 ( Sep 2018), 27-30. DOI=10.5120/ijca2018917832
Abstract
In the current days development of web technology and its growth, there is a huge amount of data present in the web for internet users and a lot of data is generated as well. Internet has become a platform for online learning, exchanging ideas and sharing opinions like Twitter, rapidly gaining popularity as they allow people to share and express their views about the trending topics. In this paper we introduced a new approach to adapt the topic model derived from news to tweets. We proposed the hashtagger+ how quickly can we suggest the hashtag mainly focuses on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly, heterogeneous and are either positive, negative, or neutral in some cases. By using various machine learning algorithms, like Naive Bayes classifier, cold start search algorithm, and hashtagger+ recommendation model.
References
- Z. Ma, A. Sun, Q. Yuan, and G. Cong, “Tagging your tweets: A probabilistic modeling of hash tag annotation in twitter,” in Proc. 23rdACMInt. Conf. Conf. Inform. Knowledge. Manage, 2014, pp. 999–1008.
- T.-A. Hoang-Vu, A. Bessa, L. Barbosa, and J. Freire, “Bridging vocabularies to link tweets and news,” in Proc. Int. Workshop Web Databases, 2014.
- Y. Gong, Q. Zhang, and X. Huang, “Hash tag recommendation using dirichlet process mixture models incorporating types of hashtags,” in Proc. Empirical Methods Natural Language Process.,2015, pp. 401–410.
- Y. Gong and Q. Zhang, “Hashtag recommendation using attention-based convolutional neural network,” in Proc. 25th Int. JointConf. Artif. Intell., 2016, pp. 2782–2788.
- B. Shi, G. Ifrim, and N. Hurley, “Learning-to-rank for real-time high-precision hash tag recommendation for streaming news,” in Proc. 25th Int. Conf. World Wide Web, 2016, pp. 1191–1202.
- K. S. Hasan and V. Ng, “Automatic key phrase extraction: A survey of the state of the art,” in Proc. 52nd Annu. Meeting Assoc. Compute. Linguistics, 2014, pp. 1262–1273.
- F. Godin, V. Slavkovikj, W. De Neve, B. Schrauwen, and R. Van deWalle, “Using topic models for twitter hashtag recommendation, “in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 593–596.
- Z. Ding, X. Qiu, Q. Zhang, and X. Huang, “Learning topical translation model for microblog hash tag suggestion,” in Proc. 23rd Int. Joint Conf. Artif. Intell., 2013, pp. 2078–2084.
- Q. Zhang, Y. Gong, X. Sun, and X. Huang, “Time-aware personalized hash tag recommendation on social media,” in Proc. 25th Int. Conf. Compute. Linguistics, 2014, pp. 203–212.
- T.-Y. Liu, “Learning to rank for information retrieval,” Foundations Trends Inform. Retrieval, vol. 3, pp. 225–331, 2009.
- S. Sedhai and A. Sun, “Hashtag recommendation for hyperlinkedtweets,” in Proc. 37th Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2014, pp. 831–834.
- F. Xiao, T. Noro, and T. Tokuda, “News-topic oriented hash tag recommendation in twitter based on characteristic co-occurrence word detection,” in 12th Int. Conf. Web Eng., 2012.
- O. Phelan, K. McCarthy, and B. Smyth, “Using twitter to recommend real-time topical news,” in Proc. 3rd ACM Conf. Recommender Syst., 2009, pp. 385–388.
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