Abstract:
E-commerce has become one of the most commodious methods of shopping because of
the technological revolution. Research shows internet shopping is much preferred by
people rather than the traditional mode of shopping. Millions of user-generated comments
are posted daily on the web, and analysis of these opinions could be more directive towards
the customer’s and manufacturers. That makes the Sentiment analysis of online reviews
one of the most sought-after research topic. This paper portrays our experimental work on
domain-specific feature-based sentiment analysis of product review. In this paper, we
worked with some fixed predefined core features of a product for presenting the customer’s
acceptance of the principal attributes of a product so that the manufacturer can improve the
basic features quality. We have proposed a feature-oriented sentiment prediction scheme.
That analyses the generated expressions from the textual reviews of a product for predicting
sentiment and assigns scores for our predefined features to present a net sentiment profile
of a product of all parameters. With 92% accuracy our sentiment detection scheme is
proved to be an effective ways to highlight the core attributes that are seems to be the most
to the purpose to the customer and manufacturer.
