What is it about?

This research aims at improving the performance of Twitter‐based sentiment analysis systems by incorporating 4 classifiers: (a) a slang classifier, (b) an emoticon classifier, (c) the SentiWordNet classifier, and (d) an improved domain‐specific classifier

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Why is it important?

It overcomes the limitations of previous methods by considering slang, emoticons, and domain‐specific terms

Perspectives

As the research aims to improve the performance of Twitter‐based sentiment classification by introducing a hybrid classification scheme. Therefore, in this context, the main contribution of the proposed work is the development of an integrated sentiment classification system based on a set of newly developed classifiers: SC, EC, SWN classifier, and domain‐specific classifier.

Shakeel Ahmad
King Abdulaziz University

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This page is a summary of: T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme, Expert Systems, August 2017, Wiley,
DOI: 10.1111/exsy.12233.
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