research-article

Sentiment Analysis Based on MapReduce: A survey

Authors:

Ghazi Al-NaymatAuthors Info & Claims

IAIT '18: Proceedings of the 10th International Conference on Advances in Information Technology

Article No.: 11, Pages 1 - 8

https://doi.org/10.1145/3291280.3291795

Published: 10 December 2018 Publication History

Abstract

Sentiment analysis is the process of analyzing people's sentiments, opinions, evaluations and emotions by studying their written text. It attracts the interest of many researchers, since it is useful for many applications, ranging from decision making to product evaluation to mention a few. Sentiment analysis can be conducted using machine-learning techniques, lexicon-based techniques or hybrid techniques that combines both. As people are more reliant on social networks such as Twitter, this has become a valuable source for sentiment analysis. However, the existence of big data frameworks require adaptation of these techniques to run within such frameworks. This paper reviews sentiment analysis techniques, focusing on the MapReduce-based analysis techniques. We found that the Naïve Bayes algorithm was the most used machine learning technique for extracting sentiments from big datasets because of its high accuracy rates. However, the dictionary-based techniques achieved better results in terms of execution time.

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Cited By

JESENKO BTHALMANN S(2024)TOOL SUPPORT FOR DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEWInternational Journal of Innovation Management10.1142/S136391962430004628:07n08Online publication date: 26-Sep-2024
https://doi.org/10.1142/S1363919624300046
Mendoń PPelc MKawala-Sterniuk AGola M(2024)ML-based Expert Products Scoring System2024 Progress in Applied Electrical Engineering (PAEE)10.1109/PAEE63906.2024.10701451(1-5)Online publication date: 24-Jun-2024
https://doi.org/10.1109/PAEE63906.2024.10701451
Kamel SEl-Attar NAbdelfattah M(2022)A Comparative Study Of Sentiment Analysis For Big Data On Hadoop2022 International Telecommunications Conference (ITC-Egypt)10.1109/ITC-Egypt55520.2022.9855697(1-7)Online publication date: 26-Jul-2022
https://doi.org/10.1109/ITC-Egypt55520.2022.9855697
Show More Cited By

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Sentiment Analysis Based on MapReduce: A survey

Index terms have been assigned to the content through auto-classification.

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IAIT '18: Proceedings of the 10th International Conference on Advances in Information Technology

December 2018

145 pages

ISBN:9781450365680

DOI:10.1145/3291280

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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KMUTT: King Mongkut's University of Technology Thonburi

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 December 2018

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IAIT 2018

IAIT 2018: The 10th International Conference on Advances in Information Technology

December 10 - 13, 2018

Bangkok, Thailand

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IAIT '18 Paper Acceptance Rate 20 of 47 submissions, 43%;

Overall Acceptance Rate 20 of 47 submissions, 43%

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Cited By

JESENKO BTHALMANN S(2024)TOOL SUPPORT FOR DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEWInternational Journal of Innovation Management10.1142/S136391962430004628:07n08Online publication date: 26-Sep-2024
https://doi.org/10.1142/S1363919624300046
Mendoń PPelc MKawala-Sterniuk AGola M(2024)ML-based Expert Products Scoring System2024 Progress in Applied Electrical Engineering (PAEE)10.1109/PAEE63906.2024.10701451(1-5)Online publication date: 24-Jun-2024
https://doi.org/10.1109/PAEE63906.2024.10701451
Kamel SEl-Attar NAbdelfattah M(2022)A Comparative Study Of Sentiment Analysis For Big Data On Hadoop2022 International Telecommunications Conference (ITC-Egypt)10.1109/ITC-Egypt55520.2022.9855697(1-7)Online publication date: 26-Jul-2022
https://doi.org/10.1109/ITC-Egypt55520.2022.9855697
Kijsanayothin PChalumporn GHewett R(2019)On using MapReduce to scale algorithms for Big Data analytics: a case studyJournal of Big Data10.1186/s40537-019-0269-16:1Online publication date: 30-Nov-2019
https://doi.org/10.1186/s40537-019-0269-1

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