2 research outputs found
Entity-Specific Sentiment Classification of Yahoo News Comments
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
ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets
Sentiment analysis is a highly subjective and challenging task. Its
complexity further increases when applied to the Arabic language, mainly
because of the large variety of dialects that are unstandardized and widely
used in the Web, especially in social media. While many datasets have been
released to train sentiment classifiers in Arabic, most of these datasets
contain shallow annotation, only marking the sentiment of the text unit, as a
word, a sentence or a document. In this paper, we present the Arabic Sentiment
Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from
analyzing tweets from the Levant region, we created a dataset of 4,000 tweets
with the following annotations: the overall sentiment of the tweet, the target
to which the sentiment was expressed, how the sentiment was expressed, and the
topic of the tweet. Results confirm the importance of these annotations at
improving the performance of a baseline sentiment classifier. They also confirm
the gap of training in a certain domain, and testing in another domain.Comment: Corpus development, Levantine tweets, multi-topic, sentiment
analysis, sentiment target, LREC-2018, OSACT-201