Difference between revisions of "Project - First Draft Proposal - Bo, Kevin, Rushin"
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We look at the following issues: | We look at the following issues: | ||
| − | * (Coarse grain clustering) Can we cluster the tweets into 6 different clusters (unsupervised, not classification), and how well will these clusters correspond to our 6 predefined clusters of tweets? | + | * Task 1: (Coarse grain clustering) Can we cluster the tweets into 6 different clusters (unsupervised, not classification), and how well will these clusters correspond to our 6 predefined clusters of tweets? |
| − | * (Fine grain clustering) Can we cluster the tweets into approximately 30 clusters, and how well will these correspond to our hash tags? | + | * Task 2: (Fine grain clustering) Can we cluster the tweets into approximately 30 clusters, and how well will these correspond to our hash tags? |
| − | * For tweets of a given topic (out of the 6), can we cluster those tweets into the approximately 5 corresponding "sub-topics" as indicated by the hash-tags? | + | * Task 3: For tweets of a given topic (out of the 6), can we cluster those tweets into the approximately 5 corresponding "sub-topics" as indicated by the hash-tags? |
| − | * Can we extract the most representative tweets for a given cluster? | + | * Task 4: Can we extract the most representative tweets for a given cluster? |
| − | == Dataset == | + | == Dataset & Evaluation == |
| − | We create a data set of at | + | We plan to create a data set of at least 1,000 tweets for each of our 30 popular/topical hash-tags. |
| + | |||
| + | For task 1, 2 and 3, we plan to use the B-cubed metric to measure the quality of clusters, using the hash-tag or higher level topic as golden classes for each tweet. | ||
== Possible Techniques == | == Possible Techniques == | ||
Blah | Blah | ||
Revision as of 14:29, 1 February 2011
Social Media Analysis (10-802) Project Proposal
Team Members
Bo Lin [bolin@cs.cmu.edu]
Kevin Dela Rosa [kdelaros@cs.cmu.edu]
Rushin Shah [rnshah@cs.cmu.edu]
Summary
Tentative Title: Fine & coarse grain clustering of tweets based on topics
We propose to tackle the problem of clustering twitter messages (tweets) for a set of six predefined topics: Politics, Sports, Technology, Entertainment, Finance, and "Just for Fun". We propose to address problem by gathering twitter data for approximately 30 popular hash tags corresponding to the different topics and performing some language and/or topic modeling on the tweets to produce a set of clusters, and then comparing those cluster against the one's defined by the different tags.
We look at the following issues:
- Task 1: (Coarse grain clustering) Can we cluster the tweets into 6 different clusters (unsupervised, not classification), and how well will these clusters correspond to our 6 predefined clusters of tweets?
- Task 2: (Fine grain clustering) Can we cluster the tweets into approximately 30 clusters, and how well will these correspond to our hash tags?
- Task 3: For tweets of a given topic (out of the 6), can we cluster those tweets into the approximately 5 corresponding "sub-topics" as indicated by the hash-tags?
- Task 4: Can we extract the most representative tweets for a given cluster?
Dataset & Evaluation
We plan to create a data set of at least 1,000 tweets for each of our 30 popular/topical hash-tags.
For task 1, 2 and 3, we plan to use the B-cubed metric to measure the quality of clusters, using the hash-tag or higher level topic as golden classes for each tweet.
Possible Techniques
Blah
