چکیده (انگلیسی):

The ability to exploit public sentiment in social media is
increasingly considered as an important tool for market understanding,
customer segmentation and stock price prediction for strategic marketing
planning and manoeuvring. This evolution of technology adoption is
energised by the healthy growth in big data framework, which caused
applications based on Sentiment Analysis (SA) in big data to become
common for businesses. However, scarce works have studied the gaps of
SA application in big data. The contribution of this paper is two-fold: (i)
this study reviews the state of the art of SA approaches. including sentiment
polarity detection, SA features (explicit and implicit), sentiment
classification techniques and applications of SA and (ii) this study reviews
the suitability of SA approaches for application in the big data frameworks,
as well as highlights the gaps and suggests future works that should be
explored. SA studies are predicted to be expanded into approaches that
utilise scalability, possess high adaptability for source variation, velocity
and veracity to maximise value mining for the benefit of the users.