Name
Title | ||
|---|---|---|
An Embeddings Based Fuzzy Linguistics Supported Model to Measure the Contextual Bias in Sentiment Polarity. |
Abstract | ||
|---|---|---|
Polarity detection plays a pivotal role in the modern cognitive research field. Common approaches to compute the polarity of a given word rely on experimental dictionaries providing always the same value, no matter where the word is used and lacking therefore adaptivity to particular contexts. In a previous article, we proposed a method supported by fuzzy linguistic modelling to quantify this contextual bias and to enable the bias -aware sentiment analysis. In this work, we implement the bias contextualization based on a word embeddings technique to capture a larger portion of the contextual bias. To show how our approach works, we measure the bias of common concepts in two different domains and discuss the results compared with our previous attempt based on document contextualization. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/978-1-61499-900-3-735 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
sentiment analysis,polarity,linguistic modelling,fuzzy logic,contextual bias,word embeddings,deep learning | Computer science,Fuzzy logic,Theoretical computer science,Natural language processing,Artificial intelligence | Conference |
Volume | ISSN | Citations |
303 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Bernabé-Moreno | 1 | 19 | 5.62 |
| Álvaro Tejeda-Lorente | 2 | 97 | 7.88 |
| Julio Herce-Zelaya | 3 | 5 | 2.07 |
| Carlos Porcel | 4 | 450 | 24.12 |
| Enrique Herrera-Viedma | 5 | 13105 | 642.24 |