Text Mining Preprocessing Techniques and its Significance based on the Dataset
Abstract
Social Network can be termed as a network of social interactions and personal relationships. The most popular social networks include Facebook, Google+ and Twitter. The content shared in such networks is usually unstructured text. Nowadays, various algorithms are being generated and analyzed to understand and extract meaningful inferences from such unstructured data. In this survey, we have the tendency to review the text mining techniques and analyze combination of those techniques to get varied textual patterns from the social networking sites. Here are some of the techniques used in preprocessing.
Keywords
Full Text:
PDFReferences
https://en.wikipedia.org/wiki/Text_mining
https://towardsdatascience.com/stemming-lemmatization-what-ba782b7c0bd8
https://www.researchgate.net/publication/308186288_Stop-Word_Removal_Algorithm_and_its_Implementation_for_Sanskrit_Language
https://sentic.net/microtext-normalization.pdf
E. Cambria and A. Hussain, Sentic computing: a common-sensebased framework for concept-level sentiment analysis. Cham.Switzerland: Springer, 2015.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.