Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
By H. H. Do, P. Prasad, Angelika Maag, Abeer Alsadoon. Expert systems with applications, 2019
611 citations recorded by the source corpus.
Abstract The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/55fed0498acf59fe1bbfe13f6d963c90b819682d
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In Artificial Intelligence
- Shuyong Gao
- Nader Tavvaf
- Weizhen Song
- Edward A. Fox
- Antonio Liotta
- Xinlei Li
- Nhan Cach Dang
- Da-Wei Yang
Citations in the corpus
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “The deep ConvNet-based models are also used to extract emotion features from raw speech input for multi-task SER [195].”
- Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “[160] provides a recent comparative review on aspect-based sentiment analysis.”
- Sentiment analysis using deep learning architectures: a review (2019, background): “2015) Identify fixed length phrases in consumer review domain (Do et al. 2019) Capture the most important n-grams feature of a sentence (Do et al.”
- Sentiment Analysis Based on Deep Learning: A Comparative Study (2020, methodology): “[53] Comparative review of sentiment analysis based on deep learning CNN, LSTM, GRU, and hybrid approaches SemEval workshop and social network sites Aspect extraction and sentiment classification”