Difference between revisions of "Esuli and Sebastiani ACT2007"
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== Related papers == | == Related papers == | ||
| − | * Paper: | + | * Paper:Mining WordNet for Fuzzy Sentiment: Sentiment tag extraction from WordNet glosses:[http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&ved=0CDcQFjAB&url=http%3A%2F%2Facl.ldc.upenn.edu%2Feacl2006%2Fmain%2Fpapers%2F13_3_andreevskaiab_262.pdf&ei=T-1oUN588LzRAZiJgYgG&usg=AFQjCNExnIUmenVLi6yXEAfFD6V0bmZ-oA&sig2=5aehscC95EPyaWhT-kMQ4g] |
| − | * Paper: | + | * Paper:Random walks on text structures:[http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&ved=0CDYQFjAB&url=http%3A%2F%2Fwww.cicling.org%2F2006%2FProceedings%2FLNCS-3878-Page249.pdf&ei=w-1oUOTSDurD0QHCkoD4Cw&usg=AFQjCNHdckJ2zL9hwyfNVSPXHgDnpCN71Q&sig2=p7W0ovvB9jAh3eU4SaPx5Q] |
| − | * Paper: | + | * Paper:Using WordNet to measure semantic orientation of adjectives:[http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CCsQFjAA&url=http%3A%2F%2Fdare.uva.nl%2Fdocument%2F154122&ei=3O1oUOLsN4Ty0gHB-oFw&usg=AFQjCNHA3HhSDVD9YOfGGwwfgsdmiJQ9cA&sig2=yaFNVfHC6TN6TBEYfzLceQ] |
| + | * Paper:SENTIWORDNET: A high-coverage lexical resouce for opinion mining[http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CC8QFjAA&url=http%3A%2F%2Fontotext.fbk.eu%2FPublications%2FsentiWN-TR.pdf&ei=Y-5oUJi-NtO50AHLqICwAg&usg=AFQjCNH7FS3TIzYvWvWROGkeXNe-24iIAg&sig2=zUzEG0BzU7dZE48KfOssiQ] | ||
== Study plan == | == Study plan == | ||
Revision as of 20:14, 30 September 2012
Contents
Citation
PageRanking WordNet Synsets: An Application to Opionion Mining,
Andrea Esuli and Fabrizio Sebastiani
Online version
PageRanking WordNet Synsets: An Application to Opionion Mining
Summary
It's not a self-contained paper, it depends on another paper heavily. It's not a creative work, and I strongly suggest not to recommend to future students.
This paper address the Sarcasm Detection problem in Twitter and Amazon review posts. They propose to use some semi-supervised learning methods to automatically generate patterns, and feed those patterns to some machine learning algorithm to detect sarcasm. However, from this paper, I have no idea how they used the unlabeled text, and they didn't provide any explanation about the classification algorithm they used, i.e. k-Nearest Neighbor.
They experimented on 2 dataset: Twitter Dataset and Amazon Dataset
Evaluation
In this paper, it proposed several feature extraction methods and a data enrichment method. In the evaluation part, it mainly compared the performance between those methods. Moreover, the authors used two settings to test the robustness, one is traditional in-domain cross validation and the other is cross domain test. It reported promising results on both settings.
Discussion
First of all, I have to say it's not a self-contained paper, it depends on another paper heavily and it's not a creative work. This paper didn't change much from the AAAI 2010 paper. The only thing that this paper did is changed some small setting of previous paper: the algorithm follows AAAI 2010 paper, the feature follows the ACL 2006 paper [1]
The weak point of the paper includes:
1. It haven't any significant change to previous methods 2. It depended on another paper so heavy that the algorithm is not complete without that paper. 3. It didn't consider any baseline algorithms. For example, they can compare their method to other semi-supervised methods or related sarcasm detection methods.
Related papers
- Paper:Mining WordNet for Fuzzy Sentiment: Sentiment tag extraction from WordNet glosses:[2]
- Paper:Random walks on text structures:[3]
- Paper:Using WordNet to measure semantic orientation of adjectives:[4]
- Paper:SENTIWORDNET: A high-coverage lexical resouce for opinion mining[5]
