Multi grain sentiment analysis is the task of simultaneously classifying sentiment expressed at different levels of granularity, as opposed to single level at a time. Models built for multi grain sentiment analysis assume fully labeled corpus at fine grained level or coarse grained level or both. Huge amount of online reviews are not fully labeled at any of the levels, but are partially labeled at both the levels. We propose a multi grain collective classification framework to not only exploit the information available at all the levels but also use intra dependencies at each level and inter dependencies between the levels. We demonstrate empirically that the proposed framework enables better performance at both the levels compared to baseline approaches.
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