Analysis of Users’ Opinions about Reasons for Divorce
الموضوعات :
Fatemeh Eghrari Solout
1
(Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.)
Mehdi Hosseinzadeh
2
(Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran)
الکلمات المفتاحية: Divorce, Keywords: social networks, users' comments, comment mining, Content analysis,
ملخص المقالة :
One of the most important issues related to knowledge discovery is the field of comment mining. Opinion mining is a tool through which the opinions of people who comment about a specific issue can be evaluated in order to achieve some interesting results. This is a subset of data mining. Opinion mining can be improved using the data mining algorithms. One of the important parts of opinion mining is the sentiment analysis in social networks. Today, the social networks contain billions of users' comments about different issues. In previous researches in this area, various methods have been used for Persian comments analysis. In these studies, preprocessing is one of the most important parts. It arranges the data set for analysis in a standard form. The number of hashtags selected for analysis is limited. To detect the positive and negative comments, knowledge extraction or neural network techniques have been used. The current research presents a method of analysis which can analyze any hashtag for each group of users and has no limitations in this regard. Type of hashtag, the number of likes, type of user and type of positive and negative sentences can be analyzed by this method. The results of simulation and comparison of divorce data set show that the proposed method has an acceptable performance.
1. Vijay.B.Raut et al, 2014. “Survey on Opinion Mining and Summarization of User Reviews on Web”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol 5(2). 1026-1030.
2. G.Angulakshmi, 2014. An Analysis on Opinion Mining: Techniques and Tools, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 7,
3. Raisa Varghese, Jayasree, 2013. “A Survey on Sentiment Analysis and Opinion Mining”, International Journal of Research in Engineering and Technology (IJRET), Vol 2 Issue 11 Nov
4. Martin Mikula and KristínaMachová, 2015.” Classification of opinions in conversational content”, IEEE 13th International Symposium on Applied Machine Intelligence and Informatics • January 22-24,
5. Sagar Bhuta and Uchit Doshi, 2014. "A review of techniques for sentiment analysis of twitter data", Issues and Challenges in Intelligent Computing Techniques (ICICT), International Conference on, pp. 583-591
6. Xiuzhen Zhang, Zhixin Zhou, Mingfang Wu, 2014. “Positive, Negative, or Mixed? Mining Blogs for Opinions”, Melbourne Australia.
7. Jianxin Li, 2014. Opinion Mining and Sentiment Analysis in Social Networks: A Retweeting Structure-Aware Approach, Utility and Cloud Computing (UCC), IEEE/ACM 7th International Conference on
8. Peiman Barnaghi, 2016. Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment, Big Data Computing Service and Applications (Big Data Service), IEEE Second International Conference on
9. R. Feldman, 2013. "Techniques and applications for sentiment analysis", Communications of the ACM, vol. 56, pp. 82-89.
10. R. Piryani, (2015) Analytical mapping of opinion mining and sentiment analysis research during 20, Information Processing and Management, Elsevier Ltd. All rights reserved
11. Wang, H., & Wang, W. (2014). Product weakness finder: An opinion-aware system through sentiment analysis. Industrial Management & Data Systems, 114 (8), 1301–1320
12. Weichselbraun, A., Gindl, S., & Scharl, A. (2014). Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge-Based Systems, 69 , 78–85
13. P. D. Turney, (2002) "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," in Proceedings of the 40th annual meeting on association for computational linguistics, , pp. 417- 424.
14. Khatibi, T., Sepehri, V. & Hamidpour, B. (2008). Efficacy of chi square method to select the parameters in the Persian text comment mining, Presented at the National Conference on Electrical Engineering, Computer and Information Technology, Hamedan, Sama educational and cultural center.
15. www.rcisp.com
16. Peikari, N. & Yaghoubi, S. (2015). The sentiment analysis in Twitter social network using the text mining techniques, International Conference on Web Researching.
17. Banitalebi, A. (2016). Opinion mining of social networks with the use of machine learning algorithms, International Conference on Computer Engineering and Information Technology.
