SenticNet: A Publicly Available Semantic Resource for Opinion Mining

By Erik Cambria, Robyn Speer, Catherine Havasi, Amir Hussain. AAAI Fall Symposium: Commonsense Knowledge, 2010

338 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8ae2d6c78c067acaa17713614c0d7d6b0a53baa8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yan Wang
2. Xiaodong Li
3. Weizhen Song
4. Wei Tao
5. Vadlamani Ravi
6. Karthik Dinakar
7. Kumar Ravi
8. Antonio Liotta

Citations in the corpus

• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”
• Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, methodology): “SenticNet was tested to measure the level of polarity in opinions of patients about the National Health Service in England [48].”
• Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying (2012, methodology): “That database has been combined with common sense and domain-speci.c texts to create a system that understands affect in free text [Cambria et al. 2010].”
• Opinion Mining and Information Fusion: A survey (2016, background): “In [69], Cambria et al. present a semantic resource for Opinion Mining based on common-sense reasoning and domain-specific ontologies, and describe the steps they took to build it.”
• A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods (2015, background): “Finally, some methods like SenticNet and LIWC, classify a sentence as positive or negative only, assuming that only polarized sentences are presented, given the context of a given application.”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “The result of sentiment analysis is typically positive, negative, or neutral [10,22].”
• Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks (2021, unknown): “ticNet is a publicly available resource for opinion mining and sentiment analysis, which uses dimensionality reduction to infer the polarity of commonsense concepts and provide an affective value for each concept [42, 43, 44, 45, 46, 47].”
• News impact on stock price return via sentiment analysis (2014, methodology): “[8,5] Semi-automatic 2 Harvard IV-4 Manual 182 Loughran and McDonald [23] Manual 6 X.”