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NLP based sentiment analysis for Twitter's opinion mining and visualization
Author(s): Maha Al-Ghalibi; Adil Al-Azzawi; Kai Lawonn
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Paper 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 Twitter and deemed to be within the confines of Data Mining. It is concerned with Natural Language Processing (NLP)-based sentiment analysis for Twitter’s opinion mining. As such, the objective of this work is to use a data mining approach of text-feature extraction, classification, and dimensionality reduction, using sentiment analysis to analyze and visualize Twitter users’ opinion. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to get word 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 BNN. 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 extract meaningful data from tweets in real-time and store them into a relational model for analysis. And then use our dimension reduction method. This will help people discover the correlation of the leading role between them, which also reflects news media’s focuses and people’s interests. This system has proved better results with respect to accuracy and efficiency in comparison with some other similar works. 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.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412A (15 March 2019); doi: 10.1117/12.2522679
Show Author Affiliations
Maha Al-Ghalibi, Univ. Koblenz-Landau (Germany)
Adil Al-Azzawi, Univ. of Missouri-Columbia (United States)
Kai Lawonn, Univ. Koblenz-Landau (Germany)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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