Name
Abstract | ||
|---|---|---|
Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level analysis of texts and are mostly able to detect only explicit expressions of sentiment. However, in many cases, emotions are not expressed by using words with an affective meaning (e.g. happy), but by describing real-life situations, which readers (based on their commonsense knowledge) detect as being related to a specific emotion. Given the challenges of detecting emotions from contexts in which no lexical clue is present, in this article we present a comparative analysis between the performance of well-established methods for emotion detection (supervised and lexical knowledge-based) and a method we propose and extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.dss.2012.05.024 | Decision Support Systems |
Keywords | Field | DocType |
commonsense knowledge,implicit expression,affective meaning,word-level analysis,comparative analysis,natural language processing,specific emotion,lexical clue,emotinet knowledge base,emotion detection,sentiment analysis,knowledge base | Commonsense knowledge,Expression (mathematics),Computer science,Sentiment analysis,Emotion detection,Natural language processing,Artificial intelligence,Knowledge base,Affect (psychology) | Journal |
Volume | Issue | ISSN |
53 | 4 | 0167-9236 |
Citations | PageRank | References |
25 | 0.80 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandra Balahur | 1 | 593 | 40.19 |
| Jesús M. Hermida | 2 | 100 | 7.89 |
| Andrés Montoyo | 3 | 678 | 67.78 |