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A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

Mostefai Abdelkader
Copyright: © 2020 |Volume: 12 |Issue: 2 |Pages: 14
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781799809005|DOI: 10.4018/IJDAI.2020070102
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MLA

Abdelkader, Mostefai. "A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis." IJDAI vol.12, no.2 2020: pp.21-34. http://doi.org/10.4018/IJDAI.2020070102

APA

Abdelkader, M. (2020). A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis. International Journal of Distributed Artificial Intelligence (IJDAI), 12(2), 21-34. http://doi.org/10.4018/IJDAI.2020070102

Chicago

Abdelkader, Mostefai. "A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis," International Journal of Distributed Artificial Intelligence (IJDAI) 12, no.2: 21-34. http://doi.org/10.4018/IJDAI.2020070102

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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.

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