Entity-Specific Sentiment Classification of Yahoo News Comments
This work addresses the problem of analyzing user sentiment across diverse entities in news comments for applications like online advertising and content personalization, but it is incremental as it builds on existing sentiment classification methods.
The paper tackled entity-specific sentiment classification of user comments on Yahoo News, a previously unaddressed problem, and proposed novel features for news comments, achieving results that outperform state-of-the-art baselines.
Sentiment classification is widely used for product reviews and in online social media such as forums, Twitter, and blogs. However, the problem of classifying the sentiment of user comments on news sites has not been addressed yet. News sites cover a wide range of domains including politics, sports, technology, and entertainment, in contrast to other online social sites such as forums and review sites, which are specific to a particular domain. A user associated with a news site is likely to post comments on diverse topics (e.g., politics, smartphones, and sports) or diverse entities (e.g., Obama, iPhone, or Google). Classifying the sentiment of users tied to various entities may help obtain a holistic view of their personality, which could be useful in applications such as online advertising, content personalization, and political campaign planning. In this paper, we formulate the problem of entity-specific sentiment classification of comments posted on news articles in Yahoo News and propose novel features that are specific to news comments. Experimental results show that our models outperform state-of-the-art baselines.