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Learning Word Vectors for Sentiment Analysis
Unsupervised vector-based approaches to semantics
can model rich lexical meanings, but
they largely fail to capture sentiment information
that is central to many word meanings and
important for a wide range of NLP tasks. We
present a model that uses a mix of unsupervised
and supervised techniques to learn word
vectors capturing semantic term–document information
as well as rich sentiment content.
The proposed model can leverage both continuous
and multi-dimensional sentiment information
as well as non-sentiment annotations.
We instantiate the model to utilize the
document-level sentiment polarity annotations
present in many online documents (e.g. star
ratings). We evaluate the model using small,
widely used sentiment and subjectivity corpora
and find it out-performs several previously
introduced methods for sentiment classification.We evaluate the model using small,
widely used sentiment and subjectivity corpora
and find it out-performs several previously
introduced methods for sentiment classification.
We also introduce a large dataset
of movie reviews to serve as a more robust
benchmark for work in this area.
2017-09-01
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