A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

Mostefai Abdelkader
ISBN13: 9781668463031|ISBN10: 1668463032|EISBN13: 9781668463048
DOI: 10.4018/978-1-6684-6303-1.ch020
Cite Chapter Cite Chapter

MLA

Abdelkader, Mostefai. "A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis." Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, IGI Global, 2022, pp. 367-381. https://doi.org/10.4018/978-1-6684-6303-1.ch020

APA

Abdelkader, M. (2022). A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis. In I. Management Association (Ed.), Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 367-381). IGI Global. https://doi.org/10.4018/978-1-6684-6303-1.ch020

Chicago

Abdelkader, Mostefai. "A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, 367-381. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6303-1.ch020

Export Reference

Mendeley
Favorite

Abstract

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.