O pinion mining in social networks

1. Opinion mining in social networks Student: Aleksandar Ponjavić 3244/2014 Mentor: ProfesordrVeljkoMilutinović

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

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

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 meaningsin various contexts

5. Opinion mining • The second task consistsin identifying the degree of objectivity and subjectivity of a text • Opinion extraction

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 taskscan greatly benefit from additional datafrom the social network (centrality).

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

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

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 byterm occurrence frequencies in the training set.

10. Opinion prediction • After document semantic orientationand after removing the degree of subjectivity • Algorithm for summarization of dataand prediction

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

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 peoplewill like or dislike product features

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

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

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

16. Questions? Thank’sfor your attention!