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This paper reports the IIT-TUDA participation in the SemEval 2016 shared Task 5 of Aspect Based Sentiment Analysis (ABSA) for sub-task 1. We describe our system incorporating domain dependency graph features, dis-tributional thesaurus and unsupervised lexical induction using an unlabeled external corpus for aspect based sentiment analysis. Overall, we(More)
In this paper, we propose a novel hybrid deep learning architecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment(More)
This paper presents an overview of the system developed and submitted as a part of our participation to the SemEval-2015 Task 10 that deals with Sentiment Analysis in Twitter. We build a Support Vector Machine (SVM) based supervised learning model for Subtask A (term level task) and Subtask B (message level task). We also participate in Subtask E viz.,(More)
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