World Wide Web

Linguistics

Philosophy

Support vector machine

Classifier (UML)

Economics

Computer science

Cognitive science

Natural language processing

Behavioral analysis

Naive Bayes classifier

Psychology

Task (project management)

Management

Social media

SemEval

Artificial intelligence

Artificial neural network

Sentiment analysis

Spelling

Convolutional neural network

Human–computer interaction

Machine learning

Cognitive Systems Research

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.

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