1 |
S. M. Mohammad, Tony, and Yang, 'Tracking Sentiment in Mail: How Genders Differ on Emotional Axes', ArXiv13096347 Cs, Sep. 2013, Accessed: Feb. 03, 2021. [Online]. Available: http://arxiv.org/abs/1309.6347
|
2 |
J. Li, Y. Rao, F. Jin, H. Chen, and X. Xiang, 'Multi-label maximum entropy model for social emotion classification over short text', Neurocomputing, vol. 210, pp. 247-256, Oct. 2016, doi: 10.1016/j.neucom.2016.03.088.
DOI
|
3 |
I. Abu Farha and W. Magdy, 'A comparative study of effective approaches for Arabic sentiment analysis', Inf. Process. Manag., vol. 58, no. 2, Art. no. 2, Mar. 2021, doi: 10.1016/j.ipm.2020.102438.
DOI
|
4 |
B. Chen, L. Fan, and X. Fu, 'Sentiment Classification of Tourism Based on Rules and LDA Topic Model', in 2019 International Conference on Electronic Engineering and Informatics (EEI), 2019, pp. 471-475.
|
5 |
A. R. Alharbi, M. Hijji, and A. Aljaedi, 'Enhancing topic clustering for Arabic security news based on k-means and topic modelling', IET Netw., 2021.
|
6 |
A. Alharbi, M. Taileb, and M. Kalkatawi, 'Deep learning in Arabic sentiment analysis: An overview', J. Inf. Sci., vol. 47, no. 1, pp. 129-140, Feb. 2021, doi: 10.1177/0165551519865488.
DOI
|
7 |
K. M. Nahar, M. Ra'ed, M. Al-Shannaq, M. Daradkeh, and R. Malkawi, 'Direct text classifier for thematic arabic discourse documents.', Int Arab J Inf Technol, vol. 17, no. 3, pp. 394-403, 2020.
DOI
|
8 |
A. Alharbi, M. Taileb, and M. Kalkatawi, 'Deep learning in Arabic sentiment analysis: An overview', J. Inf. Sci., vol. 47, no. 1, Art. no. 1, Feb. 2021, doi: 10.1177/0165551519865488.
DOI
|
9 |
M. Alhawarat and M. Hegazi, 'Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents', IEEE Access, vol. 6, pp. 42740-42749, 2018, doi: 10.1109/ACCESS.2018.2852648.
DOI
|
10 |
G. Salton, A. Wong, and C.-S. Yang, 'A vector space model for automatic indexing', Commun. ACM, vol. 18, no. 11, pp. 613-620, 1975.
DOI
|
11 |
H. M. Alghamdi and A. Selamat, 'Topic detections in Arabic Dark websites using improved Vector Space Model', in 2012 4th Conference on Data Mining and Optimization (DMO), Sep. 2012, pp. 6-12. doi: 10.1109/DMO.2012.6329790.
DOI
|
12 |
F. Bianchi, S. Terragni, D. Hovy, D. Nozza, and E. Fersini, 'Cross-lingual Contextualized Topic Models with Zero-shot Learning', ArXiv200407737 Cs, Feb. 2021, Accessed: Mar. 05, 2021. [Online]. Available: http://arxiv.org/abs/2004.07737
|
13 |
N. Alsaedi, P. Burnap, and O. Rana, 'Sensing real-world events using Arabic Twitter posts', 2016.
|
14 |
F. Ding, X. Kang, S. Nishide, Z. Guan, and F. Ren, 'A fusion model for multi-label emotion classification based on BERT and topic clustering', in International Symposium on Artificial Intelligence and Robotics 2020, Oct. 2020, vol. 11574, p. 115740D. doi: 10.1117/12.2579255.
DOI
|
15 |
A. Rafea and N. A. GabAllah, 'Topic Detection Approaches in Identifying Topics and Events from Arabic Corpora', Procedia Comput. Sci., vol. 142, pp. 270-277, 2018, doi: 10.1016/j.procs.2018.10.492.
DOI
|
16 |
A. E. Samy, S. R. El-Beltagy, and E. Hassanien, 'A Context Integrated Model for Multi-label Emotion Detection', in Arabic Computational Linguistics, vol. 142, K. Shaalan and S. R. ElBeltagy, Eds. Amsterdam: Elsevier Science Bv, 2018, pp. 61-71. doi: 10.1016/j.procs.2018.10.461.
DOI
|
17 |
I. AlAgha, 'Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives', J. Inf. Sci. Theory Pract., vol. 9, no. 1, pp. 35-53, 2021.
DOI
|
18 |
M. Abdulaziz, M. Alsolamy, A. Alotaibi, and A. Alabbas, 'Topic based Sentiment Analysis for COVID-19 Tweets'.
|
19 |
S. Wrycza and J. Maslankowski, 'Social media users' opinions on remote work during the COVID-19 pandemic. Thematic and sentiment analysis', Inf. Syst. Manag., vol. 37, no. 4, pp. 288-297, 2020.
DOI
|
20 |
A. R. Alharbi, S. D. Alharbi, A. Aljaedi, and O. Akanbi, 'Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications', in 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Sep. 2020, pp. 1-6. doi: 10.1109/IICAIET49801.2020.9257842.
DOI
|
21 |
J. Brandt et al., 'Identifying social media user demographics and topic diversity with computational social science: a case study of a major international policy forum', J. Comput. Soc. Sci., vol. 3, no. 1, pp. 167-188, Apr. 2020, doi: 10.1007/s42001-019-00061-9.
DOI
|
22 |
A. Althagafi, G. Althobaiti, H. Alhakami, and T. Alsubait, 'Arabic Tweets Sentiment Analysis about Online Learning during COVID-19 in Saudi Arabia', Int. J. Adv. Comput. Sci. Appl., pp. 620-625, 2021.
|
23 |
W. Shafqat and Y.-C. Byun, 'A recommendation mechanism for under-emphasized tourist spots using topic modeling and sentiment analysis', Sustainability, vol. 12, no. 1, p. 320, 2020.
DOI
|
24 |
M. Bekkali and A. Lachkar, 'Arabic Sentiment Analysis based on Topic Modeling', in Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society, Kenitra Morocco, Mar. 2019, pp. 1-6. doi: 10.1145/3314074.3314091.
DOI
|
25 |
T. Henriques, S. Silva, S. Bras, S. C. Soares, N. Almeida, and A. Teixeira, 'Emotionally-aware multimodal interfaces: Preliminary work on a generic affective modality', 2018, pp. 80-87.
|
26 |
H. Yin, S. Yang, and J. Li, 'Detecting topic and sentiment dynamics due to COVID-19 pandemic using social media', in International Conference on Advanced Data Mining and Applications, 2020, pp. 610-623.
|
27 |
E. Christodoulou, A. Gregoriades, M. Pampaka, and H. Herodotou, 'Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing', in Advanced Information Systems Engineering Workshops, Cham, 2020, pp. 95-108. doi: 10.1007/978-3-030-49165-9_9.
DOI
|
28 |
M. Bekkali, 'Arabic Sentiment Analysis using Different Representation Models', Int. J. Emerg. Trends Eng. Res., vol. 8, Aug. 2020, doi: 10.30534/ijeter/2020/79872020.
DOI
|
29 |
R. Baly et al., 'OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model', in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, Canada, Aug. 2017, pp. 603-610. doi: 10.18653/v1/S17-2099.
DOI
|
30 |
M. Gridach, H. Haddad, and H. Mulki, Empirical Evaluation of Word Representations on Arabic Sentiment Analysis. 2017.
|
31 |
M. Daoud and N. A. Daoud, 'Sentimental event detection from Arabic tweets', Int. J. Bus. Intell. Data Min., vol. 17, no. 4, p. 471, 2020, doi: 10.1504/IJBIDM.2020.110378.
DOI
|
32 |
S. Loria, 'textblob Documentation', Release 015, vol. 2, 2018.
|
33 |
A. R. Alharbi, S. D. Alharbi, A. Aljaedi, and O. Akanbi, 'Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications', in 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Sep. 2020, pp. 1-6. doi: 10.1109/IICAIET49801.2020.9257842.
DOI
|
34 |
Z. Mottaghinia, M.-R. Feizi-Derakhshi, L. Farzinvash, and P. Salehpour, 'A review of approaches for topic detection in Twitter', J. Exp. Theor. Artif. Intell., pp. 1-27, Jun. 2020, doi: 10.1080/0952813X.2020.1785019.
DOI
|
35 |
S. Basabain, 'Text-based Arabic Emotion Detection Challenges and Effective Approaches: A Review of the State-of-the-Art', Mar. 2021.
|
36 |
X. Chen and H. Xie, 'A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature', Cogn. Comput., vol. 12, no. 6, pp. 1097-1129, Nov. 2020, doi: 10.1007/s12559-020-09745-1.
DOI
|
37 |
N. Habbat, H. Anoun, and L. Hassouni, 'Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets', Innov. Smart Cities Appl. Vol. 4, vol. 183, p. 147, 2021.
|
38 |
R. Albalawi, T. H. Yeap, and M. Benyoucef, 'Using topic modeling methods for short-text data: A comparative analysis', Front. Artif. Intell., vol. 3, p. 42, 2020.
DOI
|
39 |
T. Zarra, R. Chiheb, R. Moumen, R. Faizi, and A. E. Afia, 'Topic and sentiment model applied to the colloquial Arabic: a case study of Maghrebi Arabic', in Proceedings of the 2017 International Conference on Smart Digital Environment, Rabat Morocco, Jul. 2017, pp. 174-181. doi: 10.1145/3128128.3128155.
DOI
|
40 |
A. Hussain et al., 'Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations', medRxiv, 2020.
|
41 |
M. Abdul-Mageed, M. Diab, and M. Korayem, 'Subjectivity and sentiment analysis of modern standard Arabic', in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, pp. 587-591.
|
42 |
A. Ghallab, A. Mohsen, and Y. Ali, 'Arabic Sentiment Analysis: A Systematic Literature Review', Appl. Comput. Intell. Soft Comput., vol. 2020, p. e7403128, Jan. 2020, doi: 10.1155/2020/7403128.
DOI
|