subhash movva | University of Minnesota, Duluth (original) (raw)

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Research paper thumbnail of Automatic Test Suite Generation for Scientific MATLAB Code

University of Minnesota M.S. thesis. July 2015. Major: Computer Science. Advisor: Andrew Brooks. ... more University of Minnesota M.S. thesis. July 2015. Major: Computer Science. Advisor: Andrew Brooks. 1 computer file (PDF); viii, 91 pages.

Research paper thumbnail of UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015

This paper provides a detailed description of the approach of our system for the Aspect-Based Sen... more This paper provides a detailed description of the approach of our system for the Aspect-Based Sentiment Analysis task of SemEval-2015. The task is to identify the Aspect Category (Entity and Attribute pair), Opinion Target and Sentiment of the reviews. For the In-domain subtask that is provided with the training data, the system is developed using a supervised technique Support Vector Machine and for the Out-of-domain subtask for which the training data is not provided, it is implemented based on the sentiment score of the vocabulary. For In-domain subtask, our system is developed specifically for restaurant data.

Research paper thumbnail of Automatic Test Suite Generation for Scientific MATLAB Code

University of Minnesota M.S. thesis. July 2015. Major: Computer Science. Advisor: Andrew Brooks. ... more University of Minnesota M.S. thesis. July 2015. Major: Computer Science. Advisor: Andrew Brooks. 1 computer file (PDF); viii, 91 pages.

Research paper thumbnail of UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015

This paper provides a detailed description of the approach of our system for the Aspect-Based Sen... more This paper provides a detailed description of the approach of our system for the Aspect-Based Sentiment Analysis task of SemEval-2015. The task is to identify the Aspect Category (Entity and Attribute pair), Opinion Target and Sentiment of the reviews. For the In-domain subtask that is provided with the training data, the system is developed using a supervised technique Support Vector Machine and for the Out-of-domain subtask for which the training data is not provided, it is implemented based on the sentiment score of the vocabulary. For In-domain subtask, our system is developed specifically for restaurant data.

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