Abstract
The paper presents a problem in identifying duplicate detection of tweets from a social network or twitter. To accomplish the screening, we use clustering process for text-based classification, and this has to be applied not for test set but also employed on the training dataset. To incorporate the knowledge of discovery and performance, we use classifier and combined clustering with classification process in the detection of spam tweets or duplicate tweets on social media. We have performed experiments on TSVM/SVM and C-SVM classification approaches on tweets and have demonstrated the efficiency related to our approach. The texts performed on the integrated cluster classification will perform better than SVM (Support Vector Machine) classification.
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Abbreviations
- SVM:
-
Support Vector Machine
- nu-SVM:
-
Regression SVM type 2
- URL:
-
Uniform resource Locator
- CK:
-
Chidambaram
- TSVM:
-
Transductive support vector machines (TSVM)
- C-SVM:
-
Classification SVM type 1
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Laxmi Narasamma, V., Sreedevi, M. (2020). A Comparative Approach for Classification and Combined Cluster Based Classification Method for Tweets Data Analysis. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 160. Springer, Singapore. https://doi.org/10.1007/978-981-32-9690-9_33
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