Difference between revisions of "Esuli and Sebastiani ACT2007"
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== Summary == | == Summary == | ||
| − | The key idea in this paper is to use [[UsesMethod::PageRank]] algorithm to rank the "most" positive or negative synset in WordNet. As PageRank is a well studied algorithm, the most challenging part is how to construct a meaningful directed graph from WordNet. In this paper, the author explored one relation: if the gloss of synset si contains a term belonging to synset sk, then draw an edge si -> sk. | + | The key idea in this paper is to use [[UsesMethod::PageRank]] algorithm to rank the "most" positive or negative synset in [[WordNet]]. As PageRank is a well studied algorithm, the most challenging part is how to construct a meaningful directed graph from WordNet. In this paper, the author explored one relation: if the gloss of synset si contains a term belonging to synset sk, then draw an edge si -> sk. |
They experimented on one benchmark dataset: [[UsesDataset::Mirco-WNOp]] | They experimented on one benchmark dataset: [[UsesDataset::Mirco-WNOp]] | ||
Revision as of 20:31, 30 September 2012
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
The key idea in this paper is to use PageRank algorithm to rank the "most" positive or negative synset in WordNet. As PageRank is a well studied algorithm, the most challenging part is how to construct a meaningful directed graph from WordNet. In this paper, the author explored one relation: if the gloss of synset si contains a term belonging to synset sk, then draw an edge si -> sk.
They experimented on one benchmark dataset: Mirco-WNOp
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]
