Authors :
Vivek Ranjan; Pragati Agrawal; Vaibhav Poddar
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3VIPD4Q
DOI :
https://doi.org/10.5281/zenodo.7226923
Abstract :
Among the various social media platforms
that dominate the internet today, Twitter has established
itself as a major player and has become a preferred
choice for expressing one’s opinion on almost
everything. Ordinary citizens aside, it is used by the
governments world over to connect directly with their
citizens, by the media houses, companies, research
organizations, and so on. Given the omnipresent role it
plays, it has thus become imperative to aggressively
pursue topic detection from Twitter so that its benefits
can be implemented in a range of applications such as
natural disaster warnings, fake news detection, and user
opinion assessment among other uses. This paper
outlines different types of topic detection techniques that
are employed frequently such as exemplar based topic
detection, clustering based topic detection, frequent
pattern mining, two level message clustering,
combination of k-means clustering methods and singular
value decomposition. While exemplar based topic
detection technique uses the most significant tweets to
detect topics, the clustering based technique uses
different clustering methods such as sequential k-means,
spherical k-means, DBSCAN, and bngram to detect the
topics. In frequent pattern mining, the FP-growth
algorithm along with its variation can be used to detect
the topics. In two level message clustering, topics are
clustered using different phases. A blend of both
algorithms is employed in the combination of k-means
clustering methods and singular value decomposition. A
detailed discussion of these topic detection techniques
has been done in this paper.
Keywords :
Text Mining; Topic Detection; Clustering; Twitter
Among the various social media platforms
that dominate the internet today, Twitter has established
itself as a major player and has become a preferred
choice for expressing one’s opinion on almost
everything. Ordinary citizens aside, it is used by the
governments world over to connect directly with their
citizens, by the media houses, companies, research
organizations, and so on. Given the omnipresent role it
plays, it has thus become imperative to aggressively
pursue topic detection from Twitter so that its benefits
can be implemented in a range of applications such as
natural disaster warnings, fake news detection, and user
opinion assessment among other uses. This paper
outlines different types of topic detection techniques that
are employed frequently such as exemplar based topic
detection, clustering based topic detection, frequent
pattern mining, two level message clustering,
combination of k-means clustering methods and singular
value decomposition. While exemplar based topic
detection technique uses the most significant tweets to
detect topics, the clustering based technique uses
different clustering methods such as sequential k-means,
spherical k-means, DBSCAN, and bngram to detect the
topics. In frequent pattern mining, the FP-growth
algorithm along with its variation can be used to detect
the topics. In two level message clustering, topics are
clustered using different phases. A blend of both
algorithms is employed in the combination of k-means
clustering methods and singular value decomposition. A
detailed discussion of these topic detection techniques
has been done in this paper.
Keywords :
Text Mining; Topic Detection; Clustering; Twitter
