A Survey on Social Digital Data-Based Sentiment Mining Techniques and Feature
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Rajesh Sisodiya, Dr. Praveen Kumar Mannepalli|
|DOI : 10.14445/22312803/IJCTT-V69I4P107|
How to Cite?
Rajesh Sisodiya, Dr. Praveen Kumar Mannepalli, "A Survey on Social Digital Data-Based Sentiment Mining Techniques and Feature," International Journal of Computer Trends and Technology, vol. 69, no. 4, pp. 34-38, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I4P107
Sentiment mining plays a very important role in business to understand the opinion of customers to improve the Customer of the products also depends on the opinion of others who have bought the products already. Reviews or feedback becomes the deciding factor for buy or sells a product. This paper has focused on elaborating the user rating behavior for a particular kind of service, product, news. Techniques developed by various researchers are discussed with their implementation dataset and outcomes. Some digital features are also detailed, which play an important role in increasing the accuracy for the prediction of sentiment class. Types of sentiment analysis and mining were also detailed. Paper has summarized evaluation parameter values of the sentiment data analysis for comparing techniques
Data mining, Online Social content, Sentiment analysis, Text Clustering.
 Q. Liu, E. Chen, H. Xiong, C. Ding, and J. Chen., Enhancing collaborative filtering by user interest expansion via personalized ranking, IEEE Transactions on Systems, Man, and CyberneticsPart B (TSMCB), 42(1) 218-233, 201.
 X. Qian, H. Feng, G. Zhao, and T. Mei., Personalized Recommendation Combining User Interest and Social Circle, IEEE Trans. Knowledge and Data Engineering, 26(7) (2014) 1487-1502.
 L. Spiliotopoulou, D. Damopoulos, Y. Charalabidis, M. Maragoudakis, and S. Gritzalis., Europe in the shadow of the financial crisis: Policy making via stance classification, in Proc. 50th Hawaii Int. Conf. Syst. Sci., (2017) 2835–2844.
 S. Hasbullah, D. Maynard, R. Z. W. Chik, F. Mohd, and M. Noor., Automated content analysis: A sentiment analysis on Malaysian government social media, in Proc. 10th Int. Conf. Ubiquitous Inf. Manage. Commun., (2016), Art. No. 30.
 P. C. G. Reddick, A. T. Chatfield, and A. Ojo., A social media text analytics framework for double-loop learning for citizen-centric public services: A case study of a local government Facebook use., Government Inf. Quart., 34(1) (2017) 110–125.
 Adnan Muhammad Shah, Xiangbin Yan, Abdul Qayyum, Rizwan Ali Naqvi, Syed Jamal Shah., Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach., International Journal of Medical Informatics, 149 (2021) 104434.
 Yingwei Yan, Jingfu Chen, Zhiyong Wang., Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations., Applied Geography, 123 (2020) 102306.
 Swagato Chatterjee., Drivers of the helpfulness of online hotel reviews: A sentiment and emotion mining approach, International Journal of Hospitality Management, 85 (2020) 102356.
 D. Deng, L. Jing, J. Yu, S. Sun and M. K. Ng., Sentiment Lexicon Construction With Hierarchical Supervision Topic Model, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(4) (2019) 704-718.
 G. Zhai, Y. Yang, H. Wang, and S. Du., Multi-attention fusion modeling for sentiment analysis of big educational data, in Big Data Mining and Analytics, 3(4) (2020) 311-319.
 B. Amina and T. Azim., SCANCPECLENS: A Framework for Automatic Lexicon Generation and Sentiment Analysis of MicroBlogging Data on China Pakistan Economic Corridor, in IEEE Access, 7 (2019) 133876-133887
 36. Hanhoon Kang, Seong Joon Yoo, Dongil Han., Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews, Expert Syst Appl, 39 (2012) 6000-6010.
 Jonathan Ortigosa-Hernández, Juan Diego Rodríguez, Leandro Alzate, Manuel Lucania, Iñaki Inza, Jose A. Lozano., Approaching sentiment analysis by using semi-supervised learning of multidimensional classifiers, Neurocomputing, 92 (2012) 98-115.
 62. Aggarwal Charu C, Zhai Cheng Xiang., Mining Text Data. Springer New York Dordrecht Heidelberg London: © Springer Science+Business Media, LLC,12 (2012).
 Cortes C, Vapnik V. Support-vector networks, presented at the Machine Learning, (1995).
 Ko Youngjoong, Seo Jungyun. Automatic text categorization by unsupervised learning. In: Proceedings of COLING-00, the 18th international conference on computational linguistics, (2000).
 Yulan He, Deyu Zhou., Self-training from labeled features for sentiment analysis, Inf Process Manage, 47 (2011) 606-616.
 Fu Xianghua, Liu Guo, Guo Yanyan, Wang Zhiqiang., Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon, Knowl-Based Syst, 37 (2013) 186- 195.
 Hu Minging, Liu Bing., Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD’04), (2004).
 Kim S, Hovy E., Determining the sentiment of opinions. In: Proceedings of the international conference on Computational Linguistics (COLING’04), (2004).
 Hatzivassiloglou V, McKeown K., Predicting the semantic orientation of adjectives. In: Proceedings of the annual meeting of the Association for Computational Linguistics (ACL’97), (1997)