Mishra to discuss sentiment analysis at hypertext and social media conference

Shubhanshu Mishra
Shubhanshu Mishra

Doctoral student Shubhanshu Mishra will present his research at the 29th ACM Conference on Hypertext and Social Media, which will be held July 9-12 in Baltimore, Maryland. The conference will focus on the role of links, linking, hypertext, and hyperlink theory on the web and beyond.

Mishra will give the talk, "Detecting the correlation between sentiment and user-level as well as text-level meta-data from benchmark corpora," which he coauthored with Assistant Professor Jana Diesner. Their study examined whether users with similar Twitter characteristics have similar sentiments and what meta-data features of tweets and users correlate with tweet sentiment. 

From the abstract: We address these two questions by analyzing six popular benchmark datasets where tweets are annotated with sentiment labels. We consider user-level as well as tweet-level meta-data features, and identify patterns and correlations of these feature with the log-odds for sentiment classes. We further strengthen our analysis by replicating this set of experiments on recent tweets from users present in our datasets; finding that most of the patterns are consistent across our analysis. Finally, we use our identified meta-data features as features for a sentiment classification algorithm, which results in around 2% increase in F1 score for sentiment classification, compared to text-only classifiers, along with a significant drop in KL-divergence. These results have potential to improve sentiment analysis applications on social media data.

Mishra has an integrated MS and BS in mathematics and computing from the Indian Institute of Technology Kharagpur. He is interested in the analysis of information generation in social networks such as those in scholarly data and social media websites. His prior projects have included systems for user sentiment profiling, active learning using human-in-the-loop design pattern, and novelty profiling in scholarly data.

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