@inproceedings{d9024887f6264ef2beaa31837c60ef0f,
title = "Sentiment analysis based on MapReduce: A survey",
abstract = "Sentiment analysis is the process of analyzing people{\textquoteright}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 Na{\"i}ve 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.",
keywords = "Big Data, Dictionary Based Analysis, Machine Learning, MapReduce Framework, Na{\"i}ve Bayes, Sentiment Analysis",
author = "Mariam Khader and Arafat Awajan and Ghazi Al-Naymat",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 10th International Conference on Advances in Information Technology, IAIT 2018 ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2018",
month = dec,
day = "10",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the 10th International Conference on Advances in Information Technology, IAIT 2018",
address = "United States",
}