Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item, it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams upon each of which the data mining algorithm can be run separately yielding more relevant results with a high efficiency.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Big Data | Data Mining | Social Media | WordNet |
@article{n2011subject,
Author={de Silva, Nisansa and Maldeniya, Danaja and Wijeratne, Chamilka},
title={{Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream}},
year={2017},
journal={arXiv preprint arXiv:1705.09995},
doi = {10.48550/ARXIV.1705.09995},
abstract={Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item, it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams upon each of which the data mining algorithm can be run separately yielding more relevant results with a high efficiency.},
misc={https://goo.gl/gbcptf,NLP:ML:BIG,https://i1.rgstatic.net/publication/317230009_Subject_Specific_Stream_Classification_Preprocessing_Algorithm_for_Twitter_Data_Stream/links/59491ecc458515db1fdb3ece/largepreview.png}
}
ActivePI: Nisansa de Silva
We propose to advance social media text analysis by combining linguistic study, preprocessing, and deep learning models to better capture sentiment, structure, and meaning in informal, large-scale user-generated text.