1. Opinion mining in social networks Student: Aleksandar Ponjavić 3244/2014 Mentor: Profesor dr Veljko Milutinović

2. Introduction Opinion mining is a type of natural language processing for tracking the mood of the public Opinion mining involves building a system to collect and categorize opinions Data – products, topic 2/16

3. Social networks Social networks are best represented as graphs Social power (member’s prestige) is centrality Centrality ▫number of links ▫number of shortest paths ▫the mean of shortest paths lengths 3/16

4. Opinion mining The first task is sentiment analysis and aims at the establishment of the polarity of the given source text Some words have different meanings in various contexts 4/16

5. Opinion mining The second task consists in identifying the degree of objectivity and subjectivity of a text Opinion extraction 5/16

6. Opinion mining The third task is aims at the discovery and/or summarization of explicit opinions of the assessed product. All three classes of opinion mining tasks can greatly benefit from additional data from the social network (centrality). 6/16

7. Document semantic orientation Ti – the i-th term of the document d |d| – is the number of terms appearing in the document d Cp and Cn – positive and negative classes score() – function that assigns positive or negative values to terms 7/16

8. Document semantic orientation Semantic orientations of individual terms are aggregated using a dictionary method This method uses two small sets of manually identified positive and negative adjectives, which serve as seed sets 8/16

9. Document semantic orientation p( t|Cp ) and p( t|Cn ) – conditional probabilities of the occurrence of the term t in positive and negative class These probabilities may be approximated by term occurrence frequencies in the training set. 9/16

10. Opinion prediction After document semantic orientation and after removing the degree of subjectivity Algorithm for summarization of data and prediction 10 /16

11. Improvements Defining more selective classes Assigning trust to credible users Using more social network data to eliminate potential spams 11 /16

12. Advantages and practical uses It can help marketers to evaluate the success of an ad campaign or new product launch. Determine which versions of a product or service are popular and identify which people will like or dislike product features 12 /16

13. Disadvantages Can be very hard to determine the word class, often depends of native language Requires strong machine learning algorithms to solve classification problem Opinions are strongly relaying on credibility of it’s users (social network) 13/16

14. Conclusion Using already existing data Fast growing technique, follow grow of social media 14/16

15. Literature Milutinović, V., “The Best Method for Presentation of Research Results”, IEEE TCCA R.F. Xu,, K.F. Wong, and Y.Q. WIA in NTCIR-7 MOAT Task, Japanese Weblog Opinion Mining G. Wang, K. Araki,. Modifying SO, Opinion Mining by Using a Balancing Factor and Detecting Neutral Expressions V. Hatzivassiloglou,, K.R. McKeown (1997). Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th Conference of the European 15/16

16. Questions? Thank’s for your attention! 16/16