A large-scale sentiment data classification for online reviews under apache spark

Samar Al-Saqqa; Ghazi Al-Naymat; Arafat Awajan

doi:10.1016/j.procs.2018.10.166

A large-scale sentiment data classification for online reviews under apache spark

Samar Al-Saqqa
, Ghazi Al-Naymat
, Arafat Awajan

Princess Sumaya University for Technology
University of Jordan

Research output: Contribution to journal › Conference article › peer-review

36 Scopus citations

Abstract

new platforms and tools that can handle large volumes of data. In this paper, we present new evaluation experiments of sentiment analysis for a large-scale dataset of online customer's reviews under Apache Spark data Processing System. Apache Spark's scalable machine learning library (MLlib) is used and three classification techniques from the library are applied; Naïve Bayes, Support vector machine, and logistic regression. The results are evaluated using the accuracy metric. Experimental results show that Support vector machine classifier outperforms Naïve Bayes and logistic regression classifiers.

Original language	English
Pages (from-to)	183-189
Number of pages	7
Journal	Procedia Computer Science
Volume	141
DOIs	https://doi.org/10.1016/j.procs.2018.10.166
State	Published - 2018
Externally published	Yes
Event	9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2018 - Leuven, Belgium Duration: 5 Nov 2018 → 8 Nov 2018

Keywords

Apache spark
Big Data
MLlib
Machine Learning
Sentiment

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10.1016/j.procs.2018.10.166

Cite this

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title = "A large-scale sentiment data classification for online reviews under apache spark",

abstract = "new platforms and tools that can handle large volumes of data. In this paper, we present new evaluation experiments of sentiment analysis for a large-scale dataset of online customer's reviews under Apache Spark data Processing System. Apache Spark's scalable machine learning library (MLlib) is used and three classification techniques from the library are applied; Na{\"i}ve Bayes, Support vector machine, and logistic regression. The results are evaluated using the accuracy metric. Experimental results show that Support vector machine classifier outperforms Na{\"i}ve Bayes and logistic regression classifiers.",

keywords = "Apache spark, Big Data, MLlib, Machine Learning, Sentiment",

author = "Samar Al-Saqqa and Ghazi Al-Naymat and Arafat Awajan",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors. Published by Elsevier Ltd.; 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2018 ; Conference date: 05-11-2018 Through 08-11-2018",

year = "2018",

doi = "10.1016/j.procs.2018.10.166",

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AU - Al-Saqqa, Samar

AU - Al-Naymat, Ghazi

AU - Awajan, Arafat

PY - 2018

Y1 - 2018

N2 - new platforms and tools that can handle large volumes of data. In this paper, we present new evaluation experiments of sentiment analysis for a large-scale dataset of online customer's reviews under Apache Spark data Processing System. Apache Spark's scalable machine learning library (MLlib) is used and three classification techniques from the library are applied; Naïve Bayes, Support vector machine, and logistic regression. The results are evaluated using the accuracy metric. Experimental results show that Support vector machine classifier outperforms Naïve Bayes and logistic regression classifiers.

AB - new platforms and tools that can handle large volumes of data. In this paper, we present new evaluation experiments of sentiment analysis for a large-scale dataset of online customer's reviews under Apache Spark data Processing System. Apache Spark's scalable machine learning library (MLlib) is used and three classification techniques from the library are applied; Naïve Bayes, Support vector machine, and logistic regression. The results are evaluated using the accuracy metric. Experimental results show that Support vector machine classifier outperforms Naïve Bayes and logistic regression classifiers.

KW - Apache spark

KW - Big Data

KW - MLlib

KW - Machine Learning

KW - Sentiment

UR - https://www.scopus.com/pages/publications/85058283689

U2 - 10.1016/j.procs.2018.10.166

DO - 10.1016/j.procs.2018.10.166

M3 - Conference article

AN - SCOPUS:85058283689

SN - 1877-0509

VL - 141

SP - 183

EP - 189

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2018

Y2 - 5 November 2018 through 8 November 2018

ER -

A large-scale sentiment data classification for online reviews under apache spark

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Keywords

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