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An improved deep learning-based approach for sentiment mining


Mohd Sharef, Nurfadhlina and Shafazand, Mohammad Yaser (2014) An improved deep learning-based approach for sentiment mining. In: 2014 4th World Congress on Information and Communication Technologies (WICT 2014), 8-11 Dec. 2014, Melaka, Malaysia. (pp. 344-348).


The sentiment mining approaches can typically be divided into lexicon and machine learning approaches. Recently there are an increasing number of approaches which combine both to improve the performance when used separately. However, this still lacks contextual understanding which led to the introduction of deep learning approaches which allows for semantic compositionality over a sentiment treebank. This paper enhances the deep learning approach with semantic lexicon so that scores can be computed in-stead merely nominal classification. Besides, neutral classification is also improved. Results suggest that the approach outperforms its original.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1109/WICT.2014.7077291
Publisher: IEEE
Keywords: Deep learning; Lexicon; Sentiment mining; SentiWordNet
Depositing User: Nabilah Mustapa
Date Deposited: 03 Jul 2017 09:36
Last Modified: 03 Jul 2017 09:36
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/WICT.2014.7077291
URI: http://psasir.upm.edu.my/id/eprint/56111
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