The impact of natural language preprocessing on big data sentiment analysis
Joint Authors
al-Nuaymat, Ghazi
Khidr, Maryam
Ujan, Arafat Atawi
Source
The International Arab Journal of Information Technology
Issue
Vol. 16, Issue 3A (s) (31 Dec. 2019), pp.506-513, 8 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2019-12-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The sentiment analysis determines peoples’ opinions, sentiments and emotions by classifying their written text into positive or negative polarity.
The sentiment analysis is important for many critical applications such as decision making and products evaluation.
Social networks are one of the main sources of sentiment analysis.
However, the huge volume of data produced by social networks requires efficient and scalable analysis techniques to be applied.
The MapReduce proved its efficiency and scalability in handling big data, thus attracted many researchers to use the MapReduce as a processing framework.
In this paper, a sentiment analysis method for big data is studied.
The method uses the Naïve Bayes algorithm for classifying texts into positive and negative polarity.
Several linguistic and Natural Language Processing (NLP)preprocessing techniques are applied on a Twitter data set, to study their impact on the accuracy of big data classification.
The preformed experiments indicates that the accuracy of the sentiment analysis is enhanced by 5%, yielding an accuracy of 73% on the Stanford Sentiment data set.
American Psychological Association (APA)
Khidr, Maryam& Ujan, Arafat Atawi& al-Nuaymat, Ghazi. 2019. The impact of natural language preprocessing on big data sentiment analysis. The International Arab Journal of Information Technology،Vol. 16, no. 3A (s), pp.506-513.
https://search.emarefa.net/detail/BIM-931852
Modern Language Association (MLA)
Khidr, Maryam…[et al.]. The impact of natural language preprocessing on big data sentiment analysis. The International Arab Journal of Information Technology Vol. 16, no. 3A (Special issue) (2019), pp.506-513.
https://search.emarefa.net/detail/BIM-931852
American Medical Association (AMA)
Khidr, Maryam& Ujan, Arafat Atawi& al-Nuaymat, Ghazi. The impact of natural language preprocessing on big data sentiment analysis. The International Arab Journal of Information Technology. 2019. Vol. 16, no. 3A (s), pp.506-513.
https://search.emarefa.net/detail/BIM-931852
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 511-512
Record ID
BIM-931852