Automatic Detection of Tags for Political Blogs Khairun-nisa Hassanali Vasileios Hatzivassiloglou The University of Texas at Dallas 1. Summary  More than 22.6 million Americans maintain web sites with regularly updated commentary (blogs), of which at least 38,500 are specifically dedicated to politics  A tool for automatically tagging of political blog posts was introduced.  Political blogs differ from other blogs as they often revolve around named entities (politicians, organizations and places). Therefore, tagging of political blog posts benefits from using basic named entity recognition to improve tagging.  Tag identification using a hybrid approach (statistical and grammatical) yield better results  Sood et. al report a precision/recall of 13.11%/22.83% whereas Wang and Davidson report a precision/recall of 45.25%/23.24%. Our recall is higher perhaps because of the domain. 7. Experimental Results 8. Conclusion 5. Tag Detection using Support Vector Machines Collect data from several blogs that tag data Preprocess data – Parse HTML and rectify errors Divide data into posts and index them by their tags Train the SVMs on the training data Output Input One classifier for each tag Blog URLs Training of SVM classifiers Detection of Tags Collect data from the blog Preprocess data – Parse HTML and rectify errors Divide data into posts Run all the classifiers on each post Output Input Top five tags associated with each post Blog URL  Many blogs tag their posts  Tags are representative of the topics discussed  Training data was collected from “Daily Kos” and “Red State”  100,000 posts from Daily Kos ( )  70,000 posts from Red State ( )  A total of 787,780 tags  Used Joachim’s SVM Light  Use the same SVM based approach with new features based on grammatical knowledge  Proper Nouns are frequently topics  Place a higher weight on proper and common nouns  Identifying entities referred by different names  Barack Obama, Obama and Barack Hussein Obama refer to the same person Fetch data from blog Preprocess data and segment into posts Perform shallow parsing Extract Noun Phrases Input Blog URL Output Top scoring nouns Extraction of Tag Nouns Fetch data from blog Preprocess data and segment into posts Perform co- reference resolution Extract entities Input Blog URL Output Top scoring entities Extraction of Tag Entities using Named Entity Recognition and Co-reference Resolution Fig. 1: Tag Detection using Support Vector Machines Fig. 2: Tag Detection using Grammatical Techniques 3. The Larger Problem  Given multiple texts from two or more blogs/political sources, answer the following questions:  On which subjects the texts, as a whole across each source, agree/disagree?  How similar are the sources’ positions?  What makes them agree/disagree?  Difficult to associate an attitude with a specific topic/subject  Many clues are implicit and appear to require deep semantic analysis  Tags can serve as a basis for bringing together posts about the same topic  Compiling a profile for each political entity: What it talks about and what its position is  Organizing groups of sources according to perspective  Tags for Political blogs are automatically detected  Tags are representative of topics  Significant topics are automatically identified using SVM and other NLP techniques 9. Future Work  Political Profile is a summary of a political entity’s (politician, political group) stance on different issues  Extract the top scoring topics along with the “entities’ sentiments” (attitudes towards topic) and select representative sentences that voice sentiments towards these topics  Aggregate information across texts according to specific criteria (poster, source, time) and quantitatively compare signatures and identify which topics are responsible for the differences 2. Political Blogs 6. Tag Detection using Grammatical Techniques 4. Why are Tags Needed? PrecisionRecallF-Score Single Word SVM27.30%60.30%37.60% + Stemming26.10%59.50%36.30% + Proper Nouns36.50%56.80%44.40% Named Entities48.40%49.10%48.70% All Combined21.10%65%31.90% Manual Scoring67.00%75%70.80% Fig 3: Results on Daily Kos PrecisionRecallF-Score Single Word SVM19.00%30.00%23.30% + Stemming22.00%30.20%25.50% + Proper Nouns46.30%54.00%49.90% Named Entities60.10%41.50%49.10% All Combined20.30%65.70%31.00% Manual Scoring47.00%62.00%53.50% Fig 4: Results on Red State  2681 posts from Daily Kos and 571 posts from Red State  Compared tags to original tags of blog post  Manually evaluated relevance of tags on a small portion of test set