Difference between revisions of "User:Rajarshd"
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*[http://malt.ml.cmu.edu/mw/index.php/A_Latent_Variable_Model_for_Geographic_Lexical_Variation#Analysis_and_Results A Latent Variable Model for Geographic Lexical Variation] | *[http://malt.ml.cmu.edu/mw/index.php/A_Latent_Variable_Model_for_Geographic_Lexical_Variation#Analysis_and_Results A Latent Variable Model for Geographic Lexical Variation] | ||
*[http://malt.ml.cmu.edu/mw/index.php/Eisenstein_et_al_ACL_2011._Discovering_Sociolinguistic_Associations_with_Structured_Sparsity Discovering Sociolinguistic Associations with Structured Sparsity] | *[http://malt.ml.cmu.edu/mw/index.php/Eisenstein_et_al_ACL_2011._Discovering_Sociolinguistic_Associations_with_Structured_Sparsity Discovering Sociolinguistic Associations with Structured Sparsity] | ||
| − | *[http://malt.ml.cmu.edu/mw/index.php/Park_et_al_CSCW_2011._The_Politics_of_Comments:_Predicting_Political_Orientation_of_News_Stories_with_Commenters%E2%80%99_Sentiment_Patterns] | + | *[http://malt.ml.cmu.edu/mw/index.php/Park_et_al_CSCW_2011._The_Politics_of_Comments:_Predicting_Political_Orientation_of_News_Stories_with_Commenters%E2%80%99_Sentiment_Patterns The Politics of Comments: Predicting Political Orientation of News Stories with Commenters’ Sentiment Patterns] |
| + | |||
| + | == Project Proposal == | ||
| + | * [http://malt.ml.cmu.edu/mw/index.php/Analyzing_User_Tweets_around_foursquare_checkins Analyzing user tweets around foursquare checkins] | ||
Latest revision as of 21:55, 8 October 2012
About Me
I am a first year Master's Student at the LTI. I believe that immense information is present out there in the social media and still a lot has to be done to extract the information out from it. I am excited about what the course has to offer and hope to learn about the latest and greatest work that has been done in this field.
Summaries of papers
- A Latent Variable Model for Geographic Lexical Variation
- Discovering Sociolinguistic Associations with Structured Sparsity
- The Politics of Comments: Predicting Political Orientation of News Stories with Commenters’ Sentiment Patterns
