Sentiment analysis of text with lossless mining

Abdul Razaq; Sanaz Kavianpour; Gavin Hales

doi:10.1109/ICECCME52200.2021.9591155

Sentiment analysis of text with lossless mining

Abdul Razaq, Sanaz Kavianpour, Gavin Hales

SDI - Cyber Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Downloads (Pure)

Abstract

Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier.

Original language	English
Title of host publication	International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2021)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9781665412629
ISBN (Print)	9781665429436
DOIs	https://doi.org/10.1109/ICECCME52200.2021.9591155
Publication status	Published - 10 Nov 2021
Event	International Conference on Electrical, Computer, Communications and Mechatronics Engineering - hybrid conference, Mauritius Duration: 7 Oct 2021 → 8 Oct 2021 http://www.iceccme.com/

Conference

Conference	International Conference on Electrical, Computer, Communications and Mechatronics Engineering
Abbreviated title	ICECCME 2021
Country/Territory	Mauritius
Period	7/10/21 → 8/10/21
Internet address	http://www.iceccme.com/

Keywords

Losless mining
Sentiment analysis
Social media analytics
Social network

Access to Document

10.1109/ICECCME52200.2021.9591155

Razaq_SentimentAnalysisOfText_Accepted_2021Accepted author manuscript, 489 KB

https://ieeexplore.ieee.org/document/9591155/

Cite this

@inproceedings{d183b9b141494905a4929ebeaa81d222,

title = "Sentiment analysis of text with lossless mining",

abstract = "Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier.",

author = "Abdul Razaq and Sanaz Kavianpour and Gavin Hales",

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booktitle = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2021)",

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Razaq, A , Kavianpour, S & Hales, G 2021, Sentiment analysis of text with lossless mining. in International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2021)., 9591155, IEEE , Piscataway, NJ, International Conference on Electrical, Computer, Communications and Mechatronics Engineering, Mauritius, 7/10/21. https://doi.org/10.1109/ICECCME52200.2021.9591155

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T1 - Sentiment analysis of text with lossless mining

AU - Razaq, Abdul

AU - Kavianpour, Sanaz

AU - Hales, Gavin

PY - 2021/11/10

Y1 - 2021/11/10

N2 - Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier.

AB - Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier.

U2 - 10.1109/ICECCME52200.2021.9591155

DO - 10.1109/ICECCME52200.2021.9591155

M3 - Conference contribution

SN - 9781665429436

BT - International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2021)

PB - IEEE

CY - Piscataway, NJ

T2 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering

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