Difference between revisions of "Tsur et al ICWSM 10"
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== Summary == | == Summary == | ||
| + | In this work, the authors introduce a novel semi-supervised approach that is able to identify sarcasm in the comments of online reviews. In order to do that, they first define a small training set, labeled by hand, which contains some very obvious sarcastic comments and some clearly non-sarcastic ones. The sarcasm levels for each of those reviews range in a scale from 1-5. | ||
| + | Using this train set, they extract two different types of features: | ||
| + | * Pattern Based | ||
| + | * Syntatctic | ||
| + | |||
| + | After the feature extraction process, in order to decide how sarcastic a new comment, drawn from a test dataset, is, they utilize a k-NN inspired classifier which works as follows: | ||
| + | |||
| + | |||
| + | == Evaluation == | ||
| + | |||
| + | '''Datasets''': | ||
| + | |||
| + | '''Metrics''': | ||
| + | |||
| + | '''Baselines''': | ||
| + | |||
| + | '''Results''': | ||
| + | |||
| + | |||
| + | == Related Papers == | ||
| + | |||
| + | |||
| + | == Study Plan == | ||
Revision as of 18:22, 30 September 2012
This a Paper that appeared at the International AAAI Conference on Weblogs and Social Media 2010
Citation
title={ICWSM--A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews},
author={Tsur, O. and Davidov, D. and Rappoport, A.},
booktitle={Proceedings of the fourth international AAAI conference on weblogs and social media},
pages={162--169},
year={2010}
Online version
Summary
In this work, the authors introduce a novel semi-supervised approach that is able to identify sarcasm in the comments of online reviews. In order to do that, they first define a small training set, labeled by hand, which contains some very obvious sarcastic comments and some clearly non-sarcastic ones. The sarcasm levels for each of those reviews range in a scale from 1-5. Using this train set, they extract two different types of features:
- Pattern Based
- Syntatctic
After the feature extraction process, in order to decide how sarcastic a new comment, drawn from a test dataset, is, they utilize a k-NN inspired classifier which works as follows:
Evaluation
Datasets:
Metrics:
Baselines:
Results:
