Volume 8 Number 1 (Jan. 2019)
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IJCCE 2019 Vol.8(1): 1-17 ISSN: 2010-3743
DOI: 10.17706/IJCCE.2019.8.1.1-17

Deep Tweets Analyzer Model for Twitter Mood Visualization and Prediction Based Deep Learning Approach

Maha Alghalibi, Adil Al-Azzawi, Kai Lawonn
Abstract—In many of today’s big data analytics applications, it might need to analyze social media feeds as well as to visualize users’ opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on design and implementation a Deep model for Twitter opinion (Mood) visualization based Deep Learning network. It is concerned with Natural Language Processing (NLP)-based sentiment analysis and Deep Learning framework for Twitter’s opinion mining visualization and classification. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to visualize the predicted mood scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using Deep Learning Model. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to visualize and extract meaningful data from tweets in real-time and store them into a Deep model for analysis. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer’s behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.

Index Terms—Deep learning, data mining and web mining, visualization in social networks, NLP and sentiment analysis, machine learning.

Maha Alghalibi is with University of Koblenz-landau, Computational Visualistics Dept., Koblenz-Germany. Adil Al-Azzawi is with University of Missouri-Columbia, EECS Dept., USA. Kai Lawonn is with University of Koblenz-landau, Computational Visualistics Dept., Koblenz-Germany.

Cite:Maha Alghalibi, Adil Al-Azzawi, Kai Lawonn, "Deep Tweets Analyzer Model for Twitter Mood Visualization and Prediction Based Deep Learning Approach," International Journal of Computer and Communication Engineering vol. 8, no. 1, pp. 1-17, 2019.

General Information

ISSN: 2010-3743
Frequency: Quarterly
Editor-in-Chief: Dr. Maode Ma
Abstracting/ Indexing: EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, and Electronic Journals Library
E-mail: ijcce@iap.org
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