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 paper address the problem [[AddressesProblem::Sentiment_analysis| Sentiment Analysis]] in word level. 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]] | ||
== Discussion == | == Discussion == | ||
| − | + | This paper addresses the problem of judging how positive or negative or neutral a word (here is more about [[WordNet]] synset) is, which is one of major task in [[AddressesProblem::sentiment analysis]]. In this paper, the authors proposed to leverage [[PageRank]] algorithm on the graph built on [[WordNet]] synset. Under the intuition that if a synset sk that contributes to the definition of synset si by virtue of its member terms occurring in the gloss of si, then the polarity of synset sk contributes to the polarity of synset si, the authors built the graph as G=(V.E) where V is all [[WordNet]] synsets and edge (si -> sk) is in E if and only if the gloss of synset si contains a term belonging to synset sk. | |
| + | |||
| + | The strong points of the paper includes: | ||
| + | 1. It first introduced PageRank into solving the words (or synset) polarity problem. | ||
| + | 2. It considered positivity and negativity separately so that it can classify words (or synset) into three categories: positive, negative and neutral. | ||
The weak point of the paper includes: | The weak point of the paper includes: | ||
| − | 1. | + | 1. This paper defined, solved and evaluated the problem on [[WordNet]] synsets, but [[WordNet]] synsets is not what we meet in real text. As a result, I think it might be better if the authors can provide a method to convert words into [[WordNet]] synsets and evaluate the proposed method on real world text. |
| − | 2. It | + | 2. It didn't consider the POS tag. We know that sense of words might vary a lot on different POS tags. As a result, even if a term in sk occurs in the gloss of si, it not necessarily suggest that the term represents the meaning of synset sk, thus sk might have different polarity with si. |
| − | 3. | + | 3. The degree of a node is associated with the length of definition, which has nothing to do with the polarity. |
== Related papers == | == Related papers == | ||
Latest revision as of 14:40, 2 October 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 paper address the problem Sentiment Analysis in word level. 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
This paper addresses the problem of judging how positive or negative or neutral a word (here is more about WordNet synset) is, which is one of major task in sentiment analysis. In this paper, the authors proposed to leverage PageRank algorithm on the graph built on WordNet synset. Under the intuition that if a synset sk that contributes to the definition of synset si by virtue of its member terms occurring in the gloss of si, then the polarity of synset sk contributes to the polarity of synset si, the authors built the graph as G=(V.E) where V is all WordNet synsets and edge (si -> sk) is in E if and only if the gloss of synset si contains a term belonging to synset sk.
The strong points of the paper includes:
1. It first introduced PageRank into solving the words (or synset) polarity problem. 2. It considered positivity and negativity separately so that it can classify words (or synset) into three categories: positive, negative and neutral.
The weak point of the paper includes:
1. This paper defined, solved and evaluated the problem on WordNet synsets, but WordNet synsets is not what we meet in real text. As a result, I think it might be better if the authors can provide a method to convert words into WordNet synsets and evaluate the proposed method on real world text. 2. It didn't consider the POS tag. We know that sense of words might vary a lot on different POS tags. As a result, even if a term in sk occurs in the gloss of si, it not necessarily suggest that the term represents the meaning of synset sk, thus sk might have different polarity with si. 3. The degree of a node is associated with the length of definition, which has nothing to do with the polarity.
Related papers
- Paper:Mining WordNet for Fuzzy Sentiment: Sentiment tag extraction from WordNet glosses:[1]
- Paper:Random walks on text structures:[2]
- Paper:Using WordNet to measure semantic orientation of adjectives:[3]
- Paper:SENTIWORDNET: A high-coverage lexical resouce for opinion mining[4]
