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TweetGrep: Weakly Supervised Joint Retrieval and Sentiment Analysis of Topical TweetsAuthors: Satarupa Guha Guha,Tanmoy Chakraborty,samik dutta,k mohit,Vasudeva Varma Conference: THE 10TH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM-16) Location cologne, Grmany Date: 2016-05-17 Report no: IIIT/TR/2016/14 AbstractAn overwhelming amount of data is generated everyday on social media, encompassing a wide spectrum of topics. With almost every business decision depending on customer opinion, mining of social media data needs to be quick and easy. For a data analyst to keep up with the agility and the scale of the data, it is impossible to bank on fully supervised techniques to mine topics and their associated sentiments from social media. Motivated by this, we propose a weakly supervised approach (named, TweetGrep) that lets the data analyst easily define a topic by few keywords and adapt a generic sentiment classifier to the topic – by jointly modeling topics and sentiments using label regularization. Experiments with diverse datasets show that TweetGrep beats the state-ofthe-art models for both the tasks of retrieving topical tweets and analyzing the sentiment of the tweets (average improvement of 4.97% and 6.91% respectively in terms of area under the curve). Further, we show that TweetGrep can also be adopted in a novel task of hashtag disambiguation, which significantly outperforms the baseline methods Full paper: pdf Centre for Search and Information Extraction Lab |
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