Timilar ppt

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Amit Joshi

Computing Semantic Similarity between two twitter users based on tweets -ppt

Technology

Timilar

Finding similarity between twitter users
Semantic Web Project

Amit Krishna Joshi
2011

Key concepts
Tweet Similarity
Twitter
Keywords
Semantic Meta-data
Normalized Google Distance

Phases

Extract tweets and Extract Semantics Integrate and Design
user meta • Consume Alchemy web user Interface
• Retrieve 100 most recent service to receive • Integrate all modules
tweets semantic metadata of • Desing user initerface
• Retrieve list of friends tweets and the links in
tweets • Perform testing
and friends
• Extract links from each • Use cosine algorithm to
tweet determine the similarity
• Implement google
similarity and computer
aggregate similarity

API and Web Services

Twittter Rest API
Get tweets
Get friends and followers of a user

Alchemy REST API:
Get semantic meta-data for the tweets and contents of links in
tweets

Readablility API
Clean up messy HTML before sending requests to Alchemy

Semantic Meta-data

html, internet, web, social
network, semantic web FieldTerminology

Workflow
1. Get tweets and user metadata

2. Extracts urls and get url contents
Two twitter
usernames
3. Clean up html contents
User Timilar
4. Get Semantic Metadata
1. Tag Cloud
2. Similarity Chart
3. Similarity Score 5. Compute Keyword and Semantic
Similarity using Cosine Algorithm

6. Compute Google Similarity for
each pair of terms

Algorithm Implementation

Cosine Similarity

Google Similarity:

Enhancements
Dynamic Similarity
Automatic score generation for all friends
Use Streaming API

Demo – Disaster !!!
Need of recorded Video Demos ☺

Demo
Web Application
Input – two twitter usernames
Output –
Tag cloud
Keyword Similarity
Semantic Similarity based on Alchemy API
Aggregated Semantic Similarity using NGD

Accomplishments
Finding similarity between any two entities
instance matching
semantic associations through
schemas/vocabularies/ontologies/classifications
Metadata and ontologies for connecting disparate data
Latent Semantics
Similarity Algorithms
Jaccard’s Coefficient
Cosine Similarity
Google Similarity

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Timilar ppt

1. Timilar Finding similarity between twitter users Semantic Web Project Amit Krishna Joshi 2011

2. Key concepts Tweet Similarity Twitter Keywords Semantic Meta-data Normalized Google Distance

3. Phases Extract tweets and Extract Semantics Integrate and Design user meta • Consume Alchemy web user Interface • Retrieve 100 most recent service to receive • Integrate all modules tweets semantic metadata of • Desing user initerface • Retrieve list of friends tweets and the links in tweets • Perform testing and friends • Extract links from each • Use cosine algorithm to tweet determine the similarity • Implement google similarity and computer aggregate similarity

4. API and Web Services Twittter Rest API Get tweets Get friends and followers of a user Alchemy REST API: Get semantic meta-data for the tweets and contents of links in tweets Readablility API Clean up messy HTML before sending requests to Alchemy

5. Semantic Meta-data html, internet, web, social network, semantic web FieldTerminology

7. Workflow 1. Get tweets and user metadata 2. Extracts urls and get url contents Two twitter usernames 3. Clean up html contents User Timilar 4. Get Semantic Metadata 1. Tag Cloud 2. Similarity Chart 3. Similarity Score 5. Compute Keyword and Semantic Similarity using Cosine Algorithm 6. Compute Google Similarity for each pair of terms

8. Algorithm Implementation Cosine Similarity Google Similarity:

9. Google Similarity

10. Project Timeline

11. Enhancements Dynamic Similarity Automatic score generation for all friends Use Streaming API

12. Demo – Disaster !!! Need of recorded Video Demos ☺

13. Demo Web Application Input – two twitter usernames Output – Tag cloud Keyword Similarity Semantic Similarity based on Alchemy API Aggregated Semantic Similarity using NGD

14. Accomplishments Finding similarity between any two entities instance matching semantic associations through schemas/vocabularies/ontologies/classifications Metadata and ontologies for connecting disparate data Latent Semantics Similarity Algorithms Jaccard’s Coefficient Cosine Similarity Google Similarity

15. Thanks!

Timilar ppt

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