Sentiment Analysis | Supervised and unsupervised learning Co-Occurrence Data |CoreNLP in java

Publié le: 04 février 2019
sur la chaîne: Ziyad Beg

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Supervised and Unsupervised Aspect Category
Detection for Sentiment Analysis With
Co-Occurrence Data Project In java.

Abstract—Using online consumer reviews as electronic word of
mouth to assist purchase-decision making has become increasingly
popular. The Web provides an extensive source of consumer
reviews, but one can hardly read all reviews to obtain a fair evaluation
of a product or service. A text processing framework that
can summarize reviews, would therefore be desirable. A subtask
to be performed by such a framework would be to find
the general aspect categories addressed in review sentences, for
which this paper presents two methods. In contrast to most existing
approaches, the first method presented is an unsupervised
method that applies association rule mining on co-occurrence
frequency data obtained from a corpus to find these aspect categories.
While not on par with state-of-the-art supervised methods,
the proposed unsupervised method performs better than several
simple baselines, a similar but supervised method, and a supervised
baseline, with an F1-score of 67%.

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