TY  - JOUR
T1  - Review on Sentiment Analysis Approaches for Social Media Data
AU - Atiqah Sia Abdullah, Nur AU - Iman Shaari, Nurul AU - Rasid Abd Rahman, Abd 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 3
SP  - 462
EP  - 467
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.462.467
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.462.467
KW  - Sentiment analysis
KW  -supervised learning
KW  -lexicon-based
KW  -politic
KW  -social media
KW  -Twitter
AB  - This study reviews sentiment analysis approaches specifically used for political research and social
media data. The comparison is based on classifier, social media type, algorithm, data review and polarity
classes. In this study, systematic literature review is used to explore the sentiment analysis approaches used
in classifying social media data. The approaches include supervised machine learning, unsupervised learning,
lexicon-based and hybridization approaches. The reviewed literatures involve data from social media such as
Twitter, Email, Youtube and websites. All the approaches are evaluated and compared based on classifier, type
of social media, data review and polarity classes. Based on the comparison, most of the researches use hybrid
approach to classify the social media data. The algorithms in hybrid approaches are combination of
lexicon-based and supervised machine learning. Most of the social media data used in these researches are
extracted from Twitter. Lexicon of dictionary based and support vector machine are used for classifying political
related tweets. There are also literatures involve Malay posts in social media. The past research uses social
media, blog and Facebook as data. Then the sentiment analysis approaches are based on support vector
machine and lexicon-based. The polarity classes involve only positive and negative or happy, unhappy and
emotionless. As a conclusion, the hybrid approach of lexicon dictionary based and support vector machine is
the best hybridization approach to classify the sentiment for the Malay political tweets.
ER  -