The focus of my PhD thesis is on extracting relationships and sentiments from textual data. I use problem-specific feature sets and machine learning techniques to accomplish this. I have a diverse background in computer science, and have collaborated and worked on projects related to computer architecture, hardware acceleration and bioinformatics. A few of the projects I have been involved in are listed below.
Sentiment Analysis of User-Generated Content
Sentiment analysis is an exciting research area using the latest techniques in natural language processing and machine learning to identify sentiments/opinions expressed in text. For instance, identifying sentiments in Amazon customer reviews can be useful to other customers as well as merchants selling similar items.
I am also interested in learning how sentiments on topics propagate through social networks. Pulse of the Tweeters is a project that seeks to identify influential users on twitter and related trending topics on Twitter, the popular microblogging social network.
Relationship extraction from Biomedical Literature
As the sizes of Biomedical literature databases like PubMed increase, it becomes important to develop algorithms and systems which are able to efficiently and accurately extract information about biological entities. Specifically, I am interested in applying text mining techniques to find protein-protein interactions in Biomedical literature.
NU-MineBench is a data mining benchmark suite containing a mix of several representative data mining applications from different application domains. This benchmark is intended for use in computer architecture research, systems research, performance evaluation, and high-performance computing.