SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis
By Erik Cambria, Yang Li, Frank Xing, Soujanya Poria, Kenneth Kwok. International Conference on Information and Knowledge Management, 2020
457 citations recorded by the source corpus.
Deep learning has unlocked new paths towards the emulation of the peculiarly-human capability of learning from examples. While this kind of bottom-up learning works well for tasks such as image classification or object detection, it is not as effective when it comes to natural language processing. Communication is much more than learning a sequence of letters and words: it requires a basic understanding of the world and social norms, cultural awareness, commonsense knowledge, etc.; all things that we mostly learn in a top-down manner. In this work, we integrate top-down and bottom-up learning via an ensemble of symbolic and subsymbolic AI tools, which we apply to the interesting problem of polarity detection from text. In particular, we integrate logical reasoning within deep learning architectures to build a new version of SenticNet, a commonsense knowledge base for sentiment analysis.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/f3d3bed63fb51315e2726837363ab3c9e89769eb
How researchers see this paper
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In Artificial Intelligence
- Soujanya Poria
- Navonil Majumder
- Devamanyu Hazarika
- Rada Mihalcea
- Erik Cambria
- Chaitanya Kulkarni
- Mayur Wankhade
- A. C. Rao
Citations in the corpus
- Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, methodology): “…et al. With the identification of speaker sensitive utterance being a significant challenge in Conversational Emotion Recognition (CER), Li et al. in Li et al. (2020) detected emotions in conversations using Multi-task Learning and BERT with emphasis on speaker-aware contextual representations.”
- Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “eart of rule-based sentiment analysis methods. Simply defined, these lexicons are dictionaries that contain sentiment annotations for their constituent words, phrases, or synsets (Joshi et al., 2017a; Cambria et al., 2020). SentiWordNet (Esuli & Sebastiani, 2006) is one such popular sentiment lex”
- A survey on sentiment analysis methods, applications, and challenges (2022, methodology): “Availability of data As NLP and sentiment analysis is a recently boomed technology, the Availability of data may also be a challenge in some cases.”
- Natural Language Processing with Commonsense Knowledge: A Survey (2021, background): “As a knowledge base, Sentic-Net [18], [34], [35] provides a set of semantics, sentics, and polarity associated with 200,000 natural language concepts.”