Word and Character Information Aware Neural Model for Emotional Analysis

Author(s): Majdi Beseiso*.

Journal Name: Recent Patents on Computer Science (Discontinued)

Volume 12 , Issue 2 , 2019

Graphical Abstract:


Background: Social media texts are often highly unstructured in accordance with the presence of hashtags, emojis and URLs occurring in abundance. Thus, a sentiment or emotion analysis on these kinds of texts becomes very difficult. The difficulty increases even more when such texts are in local languages like Arabic.

Methods: This work utilizes novel deep learning architectures in the form of character-level Convolutional Neural Network (CNN) module and the word-level Recurrent Neural Network (RNN) module to produce a hybrid architecture that makes use of the character level analysis and the word level analysis to obtain state-of-the-art results on a totally new Arabic Emotions dataset.

Results: The proposed method works the best among the traditional bag-of-words and Term Frequency and Inverse Document Frequency methods for emotion analysis. It also outperforms the state-of-the-art deep learning methods which are known to perform very well in an English corpus.

Conclusion: The proposed deep end-to-end architecture utilizes the character level information from a text through the Character CNN Module and the word level information from a text through the Word-Level RNN Module.

Keywords: Arabic, sentiment analysis, deep neural network, convolutional neural network, recurrent neural network, gated recurrent unit.

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Article Details

Year: 2019
Page: [142 - 147]
Pages: 6
DOI: 10.2174/2213275911666181119112645
Price: $58

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