Association Rule Mining in Social Network Data
- 1. Association Rule Mining in Social Network Data PRESENTED BY: HOSSEIN MOBASHER COURSE: DATA MINING
- 2. 19/2 Contents • Introduction • Related Works • The proposed Framework • Experimental Evaluation • Conclusion
- 3. 19/3 Introduction • The use of social networks has altered the way of life of online community since last decade. • Social data uses in: • Academic applications • E-commerce • Discovers the user habits and interests of different geographical online communities • Sentimental analysis of users • Purpose: Support analysts in decision-making and optimal resource management in businesses as well as web maintenance.
- 4. 19/4 Introduction (continue) • The social data is one of the powerful sources of data: • To get knowledge about social communities • Investigate the behavior and other different aspects of the online communities • User-generated contents (UGC) used to help online organizations to enhance their services based on user perspectives. • The data mining techniques are effectively exploited to discover hidden, interested and meaningful knowledge from the social data.
- 5. 19/5 Related Works • TwitterEcho • Collect data from distributed architecture (Portuguese Twittosphere) • Use of micro-blogging as the means to predict the political sentiment. • TWICALL • Discovers important events, categorizes and classifies them • NIF-T • Exploring data published on micro-blogging websites (i.e. Twitter)
- 6. 19/6 The proposed Framework • Environment for the association rule mining to discover hidden patterns from tweets.
- 7. 19/7 Collecting and preprocessing of tweets • Access tweets using Twitter API. • Received tweets are unsuitable for the subsequent processes. • Includes information which is not required for problem under consideration • Remove unnecessary information and transform them into items and related contextual features. Access data using Twitter API Remove Unnecessary Information Transform into suitable format Mapped into a transactional database
- 8. 19/8 Collecting and preprocessing of tweets • Transformed tweets are then mapped into a transactional database. • Composed of set of stems • i.e. “Imagination is more important than knowledge” may be mapped into {imagination, important, knowledge} Access data using Twitter API Remove Unnecessary Information Transform into suitable format Mapped into a transactional database
- 9. 19/9 Discovery of Correlations • Use apriori method to extract frequent itemset mining. • An association rule is usually represented as: If Body then Head • If Body happens then there are more chance that Head may also happen • It is the relationship between them • Strength of the rule depends on association rule support and confidence • The higher the strength of the rule, higher the association in between the terms. • 𝑖𝑚𝑎𝑔𝑖𝑛𝑎𝑡𝑖𝑜𝑛 ⇒ 𝑘𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 • Support = 40% • Confidence = 70%
- 10. 19/10 Taxonomy Generation • Automatically generates taxonomy based on tweet attributes (i.e. frequent keywords that are generated in the previous phase). • The more generalized or high-level concepts or correlations can be extracted. • The taxonomy nodes represent distinct terms extracted from tweet contents • Graph extraction • Graph partitioning and pruning
- 11. 19/11 Taxonomy Generation (Graph extraction) • Strong correlations are detected using previous phase result. • Generated correlations are represented in graph format • Edge: The implications present in the rule • Vertices: Items of tweet contents • 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 ⇒ 𝑊𝑜𝑟𝑙𝑑 𝑠𝑜𝑐𝑖𝑒𝑡𝑦, 𝑝𝑒𝑜𝑝𝑙𝑒 ⇒ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑝𝑒𝑎𝑐𝑒 ⇒ 𝑊𝑜𝑟𝑙𝑑 𝑠𝑜𝑐𝑖𝑒𝑡𝑦 ⇒ 𝑊𝑜𝑟𝑙𝑑 𝑠𝑜𝑐𝑖𝑒𝑡𝑦 ⇒ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦
- 12. 19/12 Taxonomy Generation (Graph partitioning and pruning) • Makes the graph compact • Prunes edges which do not have string relevant relationship by performing vertex labeling. (Label represents level of taxonomy)
- 13. 19/13 Analyzing Correlations • The selection and ranking of the significant correlations • The selection is made having • A rule schema < 𝐾𝑒𝑦𝑤𝑜𝑟𝑑,∗ > ⇒ < 𝑃𝑙𝑎𝑐𝑒,∗ > • Given interesting rule items < 𝐾𝑒𝑦𝑤𝑜𝑟𝑑, 𝑆𝑐ℎ𝑜𝑜𝑙 > ⇒ < 𝑃𝑙𝑎𝑐𝑒, 𝐿𝑜𝑛𝑑𝑜𝑛 > • The results ranked based on their support and confidence quality indexes.
- 14. 19/14 Experimental Evaluation • The proposed framework highlights famous topical subjects (i.e. European Union) • The results includes 58 transactions with 209 distinct items (i.e. keywords). • Firstly, the effectiveness is presented in two scenarios: • User behavior analysis • Topic trend analysis • Secondly, the effectiveness is presented as quality of generated taxonomies.
- 15. 19/15 User Behavior Analysis • Extracted correlations allow experts to highlight hidden and potentially interesting user behaviors. • 𝑝𝑒𝑎𝑐𝑒 ⇒ 𝑊𝑜𝑟𝑙𝑑, 𝑠𝑜𝑐𝑖𝑒𝑡𝑦 ⇒ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦, 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 ⇒ 𝑊𝑜𝑟𝑙𝑑 • Proposed framework automatically generates the taxonomy from the mined rules. • The taxonomy clearly highlights the behavior of people towards the peace.
- 16. 19/16 Topic Trend Analysis • Discovery and analysis of currently matter of contention on Twitter. • Domain expert wants to discover subjects of topical interest for Twitter users. • The taxonomy suggests that society as a general and people in particular are concerns with peace in the World.
- 17. 19/17 Quality of generated taxonomies • The evaluation of taxonomy generation is measured with • Global quality (Using geometry average) • Local quality (Degree of correlation between non-leaf and leaf nodes) • Spread (Number of nodes across the taxonomy to move from node to its root node in graph) • The results are compared with the approach of • “Evolutionary Taxonomy Construction from Dynamic Tag Space”, 2010
- 18. 19/18 Quality of generated taxonomies (continue) • Global quality remained same in both approaches. • Produced pretty balanced local quality vs. spread measurement indexes. • Proposed approach takes slightly less time comparing with the approach reported in.
- 19. 19/19 Conclusion • Present the mechanism of extracting hidden correlations between contents. • Generated correlations are helpful to understand the hidden associations among the textual and contextual features of the UGC. • Proposed approach automatically generates taxonomy. • The experimental results validate the efficiency and effectiveness of the proposed framework.
- 20. Thanks for your attentions Questions ?