In this paper, we address the problem of identifying relevant product aspects in a collection of online customer reviews. Being able to detect such aspects represents an important subtask of aspect-based review mining systems, which aim at automatically generating structured summaries of customer opinions. We cast the task as a terminology extraction problem and examine the utility of varying term acquisition heuristics, filtering techniques, variant aggregation methods, and relevance measures. We evaluate the different approaches on two distinct datasets (hotel and camera reviews). For the best configuration, we find significant improvements over a state-of-the-art baseline method.
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