EmoSenticNet version 1 in XLS format.
EmoSenticNet is a lexical resource that assigns six WordNet Affect emotion labels to SenticNet concepts. It can also de though of as an expansion of WordNet Affect emotion labels to a larger vocabulary.
This resource is useful for sentiment analysis, opinion mining, sentiment polarity detection, social network analysis, emotion analysis, etc.
This dataset was presented or used in the following papers:
- Soujanya Poria, Alexander Gelbukh, Amir Hussain, Dipankar Das, Sivaji Bandyopadhyay. Enhanced SenticNet with Affective Labels for Concept-based Opinion Mining. IEEE Intelligent Systems, vol. 28, issue 2, 2013, pp. 31–38, doi:10.1109/MIS.2013.4.
- Soujanya Poria Alexander Gelbukh Dipankar Das Sivaji Bandyopadhyay Fuzzy Clustering for Semi-Supervised Learning—Case study: Construction of an Emotion Lexicon
- Soujanya Poria, Alexander Gelbukh, Erik Cambria, Peipei Yang, Amir Hussain, Tariq Durrani. Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis. 2012 IEEE 11th International Conference on Signal Processing (ICSP), IEEE ICSP 2012. Beijing, October 21-25, 2012. Vol. 2, pp. 1251–1255; doi: 10.1109/ICoSP.2012.6491803.
- Soujanya Poria, Alexander Gelbukh, Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay. Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering. Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, SENTIRE 2012. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), December 10, 2012, Brussels, Belgium. IEEE CS Press, 2012, pp. 709–716; doi: 10.1109/ICDMW.2012.142.
It was used in the works reported in the following papers:
- Soujanya Poria, Alexander Gelbukh, Erik Cambria, Amir Hussain, Guang-Bin Huang. EmoSenticSpace: A Novel Framework for Affective Common-Sense Reasoning. Knowledge-Based Systems, vol. 69, 2014, ISSN 0950-7051, pp. 108–123, doi: 10.1016/j.knosys.2014.06.011.
Soujanya Poria Alexander Gelbukh, Basant Agarwal, Erik Cambria, Newton Howard. Common Sense Knowledge Based Personality Recognition from Text. MICAI 2013. , pp 484–496; doi: 10.1007/978-3-642-45111-9_42
Any work that uses the dataset should cite the abovementioned sources.
Before downloading these data please contact us (see contact info on www.Gelbukh.com) to see if we have a newer version.
License: free for non-commercial academic purposes. Any publication that benefited from these data must state the origin of the data and cite the abovementioned sources. We will be grateful to you if you let us know of the use of the data and of citing our papers.
Resources used for compiling this resource: