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

Maha Alghalibi, Adil Al-Azzawi, Kai Lawonn
2019 International Journal of Computer and Communication Engineering  
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
more » ... us 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.
doi:10.17706/ijcce.2019.8.1.1-17 fatcat:k7qxurio3bhvdbrzztcighltdu