Topic cluster of Streaming Tweets based on GPU-Accelerated Self Organizing Map

Group 15Chen ZhutianHuang Hengguang

Outline

Background

Pipeline and Technique

Conclusion

Background

What happen in the tweets stream?

• Unsupervised, Clustering algorithm.

• Organize large document collections according to textual similarities.

• Create visible result for searching and exploring large document collections.

WEBSOM system

• Based on Self Organizing Map.• Generate topic map for

documents.• Explore large documents just

like explore Google map.

What WEBSOM looks like?

Gap

• WEBSOM – Long document, static, long training time.

• Twitter – Short text, dynamic, streaming data

• How to adapt SOM to streaming Twitter data?

What our system looks like

Outline

Background

Pipeline and Technique

Conclusion

Pipeline and Technique

Pipeline

Detect Event

Build Dictionary

Vectorize Tweets

Reduce Dimension

SOM Cluster

Show the SOM map

Detect Event

Detect Event

• Only focus on unusual events.• How to identify abnormal events on

Twitter?

Tweets Stream

Events

Events

1. Similar to TCP’s congestion control mechanism.

2. Count the number of tweets in a moving window.

3. Weighted moving average and variance.

4. Threshold to determine whether it’s an event.

Detect Event

FIFA2014, Brazil 1:7 Germany

• Track 823 keywords.

such as “FIFA”, ”Ger”, ”Brazil”,

“#WordCup”…

• In 110 minutes.

• 100 million tweets.

• Sample 1%

Test Data

Goal!

Goal! X 3

Goal!

Time of Peak What’s happen?

4:11 First Goal!

4:25 Goal! X 3 in 3 minute

4:30 Goal!

5:07 Second Half Begin

5:25 Goal!

5:35 Goal!

5:46 Goal!

5:50 End!

Detect Event

Build Dictionary

Vectorize Tweets

Reduce Dimension

SOM Cluster

Show the SOM map

Detect Event

Detect Event

Build Dictionary

1. Remove stop words2. Stemming – Snow Balls3. Remove words whose occurrence less that

10%4. Remove words whose occurrence greater

that 50%

Build Dictionary

1. Vector Space model2. TF-IDF3. Normalization

Vectorize Tweets

𝑉 𝑖= (0.4123 ,0.12312 ,0.344 ,… )

10,000 tweets x 10,000 dimension

1+ hour for convergence

Reduce Dimension

Show the SOM map

SOM Cluster

Reduce Dimension

Vectorize Tweets

Build Dictionary

Detect Event

Reduce Dimension

Random Projection

1. No Training.2. Matrix Operation.

Based on Johnson-Lindenstrauss lemma

Show the SOM map

SOM Cluster

Reduce Dimension

Vectorize Tweets

Build Dictionary

Detect Event

SOM Cluster

What is SOM? Self-organization Map.

• Artificial Neural Network

• Unsupervised Learning• Iteration Based• Visible Result

SOM Cluster

• Sequential SOM

• Batch Type SOM Faster, Effective

SOM Cluster

Random Projection+ Batch SOM +

1 SecondHour

SOM Cluster

CUDA

20 newsgroups

• 20,000 documents.

• 20 different newsgroups.

• only in 1 group.

Test Data

http://web.ist.utl.pt/acardoso/datasets/.

60% vs

40%Train

Test

Method Random Projection

Macro Accuracy(

%)

Micro Accuracy(

%)

Renato’s SOM NO 68 67

Our Method YES 60 61

Conclusion: Random projection will result in losing precision. Hence the performance will decrease after dimension reduction.

20 Newsgroup Test

Method Random Projection

Macro Accuracy(%)

Micro Accuracy(%)

Renato’s SOM NO 68 67

Our Method YES 60 61

Matlab repeat Renato’s SOM

NO 63 62

Matlab repeat Renato’s SOM

YES 61 60

We use SOM tool box to repeat Renato’s experiment totally.

20 Newsgroup Test

FIFA Data

FIFA Data

FIFA Data

Conclusion

• 2 algorithms• 3 sets of

experiment• 1 prototype

system• 1 case study

Conclusion

Thanks for Watching

Q & A