Name
Title | ||
|---|---|---|
A Fuzzy Linguistics Supported Model to Measure the Contextual Bias in Sentiment Polarity. |
Abstract | ||
|---|---|---|
The polarity detection problem typically relies on experimental dictionaries, where terms are assigned polarity scores lacking contextual information. As a matter of fact, the polarity is highly dependant on the domain or community it is analysed, so we can speak of a contextual bias. We propose a method supported by fuzzy linguistic modelling to quantify this contextual bias and to enable the bias-aware sentiment analysis. To show how our approach work, we measure the bias of common concepts in two different domains and discuss the results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-66830-7_19 | ADVANCES IN FUZZY LOGIC AND TECHNOLOGY 2017, VOL 1 |
Keywords | Field | DocType |
Sentiment analysis,Polarity,Linguistic modelling,Fuzzy logic,Contextual bias | Contextual information,Sentiment analysis,Fuzzy logic,Psychology,Matter of fact,Natural language processing,Artificial intelligence,Dependant,Linguistics | Conference |
Volume | ISSN | Citations |
641 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Bernabé-Moreno | 1 | 19 | 5.62 |
| Álvaro Tejeda-Lorente | 2 | 97 | 7.88 |
| Carlos Porcel | 3 | 450 | 24.12 |
| Enrique Herrera-Viedma | 4 | 13105 | 642.24 |