Collective Sentiment Classification Based on User Leniency and Product Popularity
نویسندگان
چکیده
منابع مشابه
Collective Sentiment Classification Based on User Leniency and Product Popularity
We propose a method of collective sentiment classification that assumes dependencies among labels of an input set of reviews. The key observation behind our method is that the distribution of polarity labels over reviews written by each user or written on each product is often skewed in the real world; intolerant users tend to report complaints while popular products are likely to receive prais...
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Classical approaches to sentiment classification exploit only textual features in a given review and are not aware of the personality of the user or the public sentiment toward the target product. In this paper, we propose a model that can accurately estimate the sentiment polarity by referring to the user leniency and product popularity computed during testing. For decoding with this model, we...
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Document-level sentiment classification aims to predict user’s overall sentiment in a document about a product. However, most of existing methods only focus on local text information and ignore the global user preference and product characteristics. Even though some works take such information into account, they usually suffer from high model complexity and only consider wordlevel preference ra...
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Mass processing of social media posts has been brought to scientists’ attention during the last decade. The massive growth of online social networks, like Twitter and Facebook, have created a need for determining peoples’ opinions and moods through these means. This thesis constitutes a research on measuring users’ sentiment upon a particular subject by analyzing their posts. Establishing an ef...
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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...
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ژورنال
عنوان ژورنال: Journal of Natural Language Processing
سال: 2014
ISSN: 1340-7619
DOI: 10.5715/jnlp.21.541