| 41399 |
Creator |
b040f63fe07909831fea669121318768 |
| 41399 |
Creator |
b8d521a6173aa941c32cd5686f640bfa |
| 41399 |
Creator |
cdd7ce296512d3575bcad552e19f8995 |
| 41399 |
Creator |
def33917ff93e2908aacd52ed8b81d9f |
| 41399 |
Date |
2014-10-19 |
| 41399 |
Is Part Of |
p03029743 |
| 41399 |
Is Part Of |
repository |
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abstract |
Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly
expressed through affective words. Nevertheless, sentiment is often implicitly expressed
via latent semantic relations, patterns and dependencies among words in tweets. In
this paper, we propose a novel approach that automatically captures patterns of words
of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment
pattern extraction, our proposed approach does not rely on external and fixed sets
of syntactical templates/patterns, nor requires deep analyses of the syntactic structure
of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment
analysis tasks by using the extracted semantic patterns as classification features
in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance
of our patterns against 6 state-of-the-art baselines. Results show that our patterns
consistently outperform all other baselines on all datasets by 2.19% at the tweet-level
and 7.5% at the entity-level in average F-measure. |
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authorList |
authors |
| 41399 |
presentedAt |
ext-eeeaa06b86da31a6eebeaa93767f4686 |
| 41399 |
status |
peerReviewed |
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uri |
http://data.open.ac.uk/oro/document/270803 |
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uri |
http://data.open.ac.uk/oro/document/270832 |
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uri |
http://data.open.ac.uk/oro/document/271176 |
| 41399 |
uri |
http://data.open.ac.uk/oro/document/271177 |
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uri |
http://data.open.ac.uk/oro/document/271178 |
| 41399 |
uri |
http://data.open.ac.uk/oro/document/271179 |
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uri |
http://data.open.ac.uk/oro/document/271180 |
| 41399 |
volume |
8797 |
| 41399 |
type |
AcademicArticle |
| 41399 |
type |
Article |
| 41399 |
label |
Saif, Hassan ; He, Yulan ; Fernández, Miriam and Alani, Harith (2014). Semantic
patterns for sentiment analysis of Twitter. In: The Semantic Web – ISWC 2014, Springer
International Publishing, pp. 324–340. |
| 41399 |
label |
Saif, Hassan ; He, Yulan ; Fernández, Miriam and Alani, Harith (2014). Semantic
patterns for sentiment analysis of Twitter. In: The Semantic Web – ISWC 2014, Springer
International Publishing, pp. 324–340. |
| 41399 |
Publisher |
ext-6c8b7c40a5167b142d7fb1354cd46407 |
| 41399 |
Title |
Semantic patterns for sentiment analysis of Twitter |
| 41399 |
in dataset |
oro |