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1. Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information.

Author

Alharbi, Ahmed Sulaiman M. and de Doncker, Elise

Subjects
*ASSOCIATIVE memory (Psychology), *SENTIMENT analysis, *ARTIFICIAL neural networks, *SUPPORT vector machines, *SOCIAL media
Abstract

Abstract Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task. [ABSTRACT FROM AUTHOR]

Published
2019
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2. Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information

Author

Elise de Doncker and Ahmed Sulaiman M. Alharbi

Subjects
Computer science, Cognitive Neuroscience, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Experimental and Cognitive Psychology, 02 engineering and technology, computer.software_genre, Convolutional neural network, 03 medical and health sciences, Naive Bayes classifier, 0302 clinical medicine, Artificial Intelligence, Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, Social media, Artificial neural network, business.industry, Deep learning, Sentiment analysis, Support vector machine, 020201 artificial intelligence & image processing, Artificial intelligence, InformationSystems_MISCELLANEOUS, business, computer, 030217 neurology & neurosurgery, Software, Natural language processing
Abstract

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task.

Published
2019