Sentiment Analysis Based on Deep Learning: A Comparative Study
| Autor: | Nhan Cach Dang, María N. Moreno-García, Fernando De la Prieta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Word embedding Computer Networks and Communications Computer science neural network lcsh:TK7800-8360 02 engineering and technology computer.software_genre Public opinion Machine Learning (cs.LG) Computer Science - Information Retrieval 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering natural language processing Computer Science - Computation and Language Artificial neural network business.industry Deep learning lcsh:Electronics Sentiment analysis deep learning 020207 software engineering Term (time) Range (mathematics) machine learning Hardware and Architecture Control and Systems Engineering sentiment analysis Signal Processing 020201 artificial intelligence & image processing Artificial intelligence InformationSystems_MISCELLANEOUS business Computation and Language (cs.CL) computer Information Retrieval (cs.IR) Natural language processing |
| Zdroj: | Electronics Volume 9 Issue 3 Electronics, Vol 9, Iss 3, p 483 (2020) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics9030483 |
| Popis: | The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users&rsquo opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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