Abstract
Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words' sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.
| Original language | English |
|---|---|
| Title of host publication | SSA-SMILE 2014 : joint proceedings of SSA 2014 and SMILE 2014 |
| Subtitle of host publication | joint proceedings of the 1st workshop on Semantic Sentiment Analysis (SSA2014), and the workshop on Social Media and Linked Data for Emergency Response (SMILE 2014) co-located with 11th European Semantic Web Conference (ESWC 2014) |
| Editors | Aldo Gangemi, Harith Alani, Malvina Nissim, Erik Cambria, Diego Reforgiato Recupero |
| Publisher | CEUR-WS.org |
| Pages | 5-12 |
| Number of pages | 8 |
| Publication status | Published - 2014 |
| Event | 1st workshop on Semantic Sentiment Analysis / workshop on Social Media and Linked Data for Emergency Response / co-located with 11th European Semantic Web Conference (ESWC 2014) - Crete, Greece Duration: 25 May 2014 → 25 May 2014 |
Publication series
| Name | CEUR workshop proceedings |
|---|---|
| Volume | 1329 |
| ISSN (Electronic) | 1613-0073 |
Workshop
| Workshop | 1st workshop on Semantic Sentiment Analysis / workshop on Social Media and Linked Data for Emergency Response / co-located with 11th European Semantic Web Conference (ESWC 2014) |
|---|---|
| Abbreviated title | SSA-SMILE 2014 (ESWC 2014) |
| Country | Greece |
| City | Crete |
| Period | 25/05/14 → 25/05/14 |
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Bibliographical note
Saif, H; He, Y; Fernández, M; Alani, H 2014 'Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter', Proc. SSA-SMILE 2014, http://ceur-ws.org/Vol-1329/paper_2.pdfKeywords
- lexicon adaptation
- semantics
- sentiment analysis
Cite this
}
Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter. / Saif, Hassan; He, Yulan; Fernández, Miriam; Alani, Harith.
SSA-SMILE 2014 : joint proceedings of SSA 2014 and SMILE 2014: joint proceedings of the 1st workshop on Semantic Sentiment Analysis (SSA2014), and the workshop on Social Media and Linked Data for Emergency Response (SMILE 2014) co-located with 11th European Semantic Web Conference (ESWC 2014). ed. / Aldo Gangemi; Harith Alani; Malvina Nissim; Erik Cambria; Diego Reforgiato Recupero. CEUR-WS.org, 2014. p. 5-12 2 (CEUR workshop proceedings; Vol. 1329).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter
AU - Saif, Hassan
AU - He, Yulan
AU - Fernández, Miriam
AU - Alani, Harith
N1 - Saif, H; He, Y; Fernández, M; Alani, H 2014 'Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter', Proc. SSA-SMILE 2014, http://ceur-ws.org/Vol-1329/paper_2.pdf
PY - 2014
Y1 - 2014
N2 - Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words' sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.
AB - Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words' sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.
KW - lexicon adaptation
KW - semantics
KW - sentiment analysis
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84926328342&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84926328342
T3 - CEUR workshop proceedings
SP - 5
EP - 12
BT - SSA-SMILE 2014 : joint proceedings of SSA 2014 and SMILE 2014
A2 - Gangemi, Aldo
A2 - Alani, Harith
A2 - Nissim, Malvina
A2 - Cambria, Erik
A2 - Reforgiato Recupero, Diego
PB - CEUR-WS.org
ER -