- Tytuł:
- Comparative study of CNN and LSTM for opinion mining in long text
- Autorzy:
-
Yousf, Siham
Rhanoui, Maryem
Mounia, Mikram - Powiązania:
- https://bibliotekanauki.pl/articles/1837369.pdf
- Data publikacji:
- 2020
- Wydawca:
- Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
- Tematy:
-
deep learning
long text opinion mining
CNN
LSTM - Opis:
- The digital revolution has encouraged many companies to set up new strategic and operational mechanisms to supervise the flow of information published about them on the Web. Press coverage analysis is a part of sentiment analysis that allows companies to discover the opinion of the media concerning their activities, products and services. It is an important research area, since it involves the opinion of informed public such as journalists, who may influence the opinion of their readers. However, from an implementation perspective, the analysis of the opinion from media coverage encounters many challenges. In fact, unlike social networks, the Media coverage is a set of large textual documents written in natural language. The training base being huge, it is necessary to adopt large-scale processing techniques like Deep Learning to analyze their content. To guide researchers to choose between one of the most commonly used models CNN and LSTM, we compare and apply both models for opinion mining from long text documents using real datasets.
- Źródło:
-
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 3; 50-55
1897-8649
2080-2145 - Pojawia się w:
- Journal of Automation Mobile Robotics and Intelligent Systems
- Dostawca treści:
- Biblioteka Nauki