| 1 |
Correlação entre traços de personalidade e emoções de realização com engajamento e desempenho em MOOCS
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
|
|
Adeel, Ahsan; Guellil, Imane; Gogate, Mandar; Azouaou, Faical; Dashtipour, Kia; Hussain, Amir; Kashani, Reza; Hachani, Ala-Eddine; Ieracitano, Cosimo; Benali, Fodil. - : Springer, 2021
|
|
Abstract:
In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).
|
|
Keyword:
Arabic; Arabic dialect; Arabizi; Sentiment analysis; Translation; Transliteration
|
|
URL: https://napier-surface.worktribe.com/file/2866999/1/A%20Semi-supervised%20Approach%20For%20Sentiment%20Analysis%20Of%20Arab%20%28ic%2B%20Izi%29%20Messages%3A%20Application%20To%20The%20Algerian%20Dialect
https://doi.org/10.1007/s42979-021-00510-1
|
|
BASE
|
|
Hide details
|
|
|
| 6 |
A Fuzzy Approach to Sentiment Analysis at the Sentence Level
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Sentiment Analysis of Amazon Electronic Product Reviews using Deep Learning
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Political analytics on election candidates and their parties in context of the US Presidential elections 2020
|
|
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Candidates rather than context shape campaign sentiment in French Presidential Elections (1965–2017)
|
|
|
|
In: French Politics ; 19 (2021), 4. - S. 394-420. - Springer. - ISSN 1476-3419. - eISSN 1476-3427 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
On the Logistical Difficulties and Findings of Jopara Sentiment Analysis
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis
|
|
|
|
In: IEEE Access, vol. 9, pp. 39626-39635, 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Hacia el análisis de sentimientos en euskera ; Towards sentiment analysis in Basque
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
The impact of Arabic part of speech tagging on sentiment analysis: A new corpus and deep learning approach
|
|
|
|
In: Test Series for Scopus Harvesting 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Using Twitter Streams for Opinion Mining: a case study on Airport Noise
|
|
|
|
In: ISSN: 1865-0929 ; Communications in Computer and Information Science ; https://hal.archives-ouvertes.fr/hal-03018998 ; Communications in Computer and Information Science, Springer Verlag, 2020, ⟨10.1007/978-3-030-44900-1_10⟩ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Affective behavior modeling on social networks ; Modélisation des sentiments sur les réseaux sociaux
|
|
|
|
In: https://tel.archives-ouvertes.fr/tel-03339755 ; Social and Information Networks [cs.SI]. Université Montpellier, 2020. English. ⟨NNT : 2020MONTS073⟩ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Clickbait detection using multimodel fusion and transfer learning ; Détection de clickbait utilisant fusion multimodale et apprentissage par transfert
|
|
|
|
In: https://tel.archives-ouvertes.fr/tel-03139880 ; Social and Information Networks [cs.SI]. Institut Polytechnique de Paris, 2020. English. ⟨NNT : 2020IPPAS025⟩ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Neural approach for Arabic sentiment analysis ; Une approche neuronale pour l’analyse d’opinions en arabe
|
|
|
|
In: https://tel.archives-ouvertes.fr/tel-03084468 ; Informatique et langage [cs.CL]. Université du Maine; Université de Sfax (Tunisie), 2020. Français. ⟨NNT : 2020LEMA1022⟩ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Hotel Review Sentiment Analysis using Natural Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
