Open AccessFeature Selection Methods for Mining Social Media
Author(s) -
V. Mageshwari,
I. Laurence Aroquiaraj
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a6120.119119
Subject(s) - dimensionality reduction , preprocessor , feature selection , computer science , social media , principal component analysis , singular value decomposition , data pre processing , artificial intelligence , curse of dimensionality , feature extraction , data mining , sentiment analysis , selection (genetic algorithm) , pattern recognition (psychology) , information retrieval , world wide web
People can share their thoughts and opinion through Social Media which can easily widespread. So many public issues and political views are also discussed on social media. HIV/AIDS is also one of the important topics discussed. This work aims to classify HIV/AIDS related twitter data. Since the twitter data is highly dimensional, it is essential to do reduce dimensionality of the data to attain better classification results. Tweets are collected using keyword search and necessary preprocessing steps are carried out. Then feature extraction methods such as Bag of Words (BOW) model and TF-IDF are implemented. Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) techniques are used for dimensionality reduction. Finally, classification is carried out and the results are discussed.