A real-time big data sentiment analysis for iraqi tweets using spark streaming - Nashwan Dheyaa Zaki; Nada Yousif Hashim; Yasmin Makki Mohialden; Mostafa Abdulghafoor Mohammed; Tole Sutikno; Ahmed Hussein Ali (Bulletin of Electrical Engineering and Informatics, 2020)
The scale of data streaming in social networks, such as Twitter, is increasing exponentially. Twitter is one of the most important and suitable big data sources for machine learning research in terms of analysis, prediction, extract knowledge, and opinions. People use Twitter platform daily to express their opinion which is a fundamental fact that influence their behaviors. In recent years, the flow of Iraqi dialect has been increased, especially on the Twitter platform. Sentiment analysis for different dialects and opinion mining has become a hot topic in data science researches. In this paper, we will attempt to develop a real-time analytic model for sentiment analysis and opinion mining to Iraqi tweets using spark streaming, also create a dataset for researcher in this field. The Twitter handle Bassam AlRawi is the case study here. The new method is more suitable in the current day machine learning applications and fast online prediction.