| dc.description.abstract | Abstract: Social Media Sentiment Analysis is
a field of study with a vast number of
applications. One important application is
analysing customer behaviours using the
results of social media sentiment analysis
which is a great tool that decision-makers
can utilize. There are several studies
conducted about this field. This paper
presents the results of a systematic literature
review conducted on the existing studies
which would be beneficial for developers and
researchers interested in this field. This is a
preliminary SLR in which, research papers
published in journals and conferences until
2020 were collected from 7 electrical
databases. Initially, 86 studies were found
and 5 most relevant studies derived through
specific inclusion and exclusion criteria were
investigated to analyse the current status of
research, approaches and methods used,
results, limitations, existing gaps and future
recommendations by researchers. The
results of this study suggest that hybrid
models that combine lexicons and machine
learning classification models produce more
accurate results in sentiments analysis.
Researchers have attempted to conduct
sentiment analysis considering various
components of social media text data:
punctuation, emoji and emoticons, negations,
acronyms and slangs etc. Most studies focus
on various applications of social media
sentiment analysis beneficial for
understanding and interacting with
customers. Such as identifying how cultural
and economical differences, occurrence of
various events impact consumer purchasing
behaviours, dealing with negative sentiment
shifts, segmenting consumers into groups
and even predicting sales performance etc.
This study makes a significant contribution
by providing a comprehensive and up-todate
review of the previous attempts made in
the selected domain. | en_US |
