16,800 research outputs found
Deep Learning for User Comment Moderation
Experimenting with a new dataset of 1.6M user comments from a Greek news
portal and existing datasets of English Wikipedia comments, we show that an RNN
outperforms the previous state of the art in moderation. A deep,
classification-specific attention mechanism improves further the overall
performance of the RNN. We also compare against a CNN and a word-list baseline,
considering both fully automatic and semi-automatic moderation
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
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