Naseem, Usman, and Musial, Katarzyna (2019) DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis. In: Proceedings of the 15th IAPR International Conference on Document Analysis and Recognition. pp. 953-958. From: ICDAR 2019: 15th IAPR International Conference on Document Analysis and Recognition, 20-25 September 2019, Sydney, NSW, Australia.

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Abstract

The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.

Item ID:	79243
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-3014-9
Copyright Information:	© 2019 IEEE.
Date Deposited:	10 Jul 2023 23:26
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
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