Deep learning for sentiment analysis
Bibliographic Data
| ID | 4775551 |
|---|---|
| Authors | Lina Maria Rojas‐barahona, Lina M Rojas-Barahona (0009-0009-8439-4695, University of Cambridge, corresponding author) |
| Year | 2016 |
| Volume | 10 |
| Issue | 12 |
| Pages | 701-719 |
| Publication date | 2016-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Language and Linguistics Compass (JOURNAL) |
| Journal identifiers | ISSN: 1749-818X • E-ISSN: 1749-818X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/lnc3.12228 |
| OpenAlex | W2565145181 |
| Language | EN |
| Citations received | 6 |
| References cited | 34 |
Research and industry are becoming more and more interested in finding automatically thepolarised opinionof the general public regarding a specific subject. The advent ofsocial networkshas opened the possibility of having access tomassive blogs,recommendations, andreviews. The challenge is to extract thepolarityfrom these data, which is a task ofopinion miningorsentiment analysis. The specific difficulties inherent in this task include issues related tosubjectiveinterpretation andlinguistic phenomenathat affect the polarity of words. Recently,deep learninghas become a popular method of addressing this task. However, different approaches have been proposed in the literature. This article provides an overview of deep learning for sentiment analysis in order to place these approaches in context
Data science · Deep learning · Linguistics · Natural language processing · Political science · Public opinion · Sentiment analysis · World Wide Web · Advanced Text Analysis Techniques · Computer Science · History · Sentiment Analysis and Opinion Mining · Topic Modeling · Artificial Intelligence
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| Citations per year | 1 |
|---|---|
| Citation span | 2020 - 2026 (7) |
| Highly cited | No |