Inproceedings
عنوان الكتاب :
5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)
المكان :
Marrakeche, Morroco
ISBN : 978-1-7281-6175-4
الناشر :
معلومات
الفترة : May 2020
الصفحات : 1-6
التفاصيل
Graph-based Model for Negative e-WOM Influence in Social Media
Abderraouf Dembri Mohamed Gharzouli
Nowadays, several companies use social media marketing to increase profit and control the market. The customer's feedback has a powerful influence on company reputation by conveying their experience in social media. Customers exchange their feedback about the services using electronic Word-of-Mouth (e-WOM). Negative feedback could help companies improve their service to increase profit. In this work, we propose an approach to determine the effect of negative e-WOM relating to a company's products or services. Firstly, we apply a machine-learning algorithm called random forest to classify e-WOM on three classes based on polarity: Positive, negative, or neutral. Secondly, we group negative e-WOM into different clusters based on their topics using a similarity method named cosine similarity. Thirdly, we generate an influence graph of negative e-WOM based on time precedence and social ties. Finally, we analyze the resulted graph to identify risk patterns and convey useful information. The provided method is implemented using Python and is tested with collected data.
الكلمات المفتاحية :
Social media marketing Electronic word-of-mouth Social commerce Machine learning
مرجع الإقتباس :
misc-lab-349
DOI :
10.1109/CloudTech49835.2020.9365914
الرابط :
Texte intégral
ACM :
A. Dembri and M. Gharzouli. 2020. Graph-based Model for Negative e-WOM Influence in Social Media. In Proceedings of the 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), Marrakeche, Morroco (May 2020), IEEE, 1-6. DOI: https://doi.org/10.1109/CloudTech49835.2020.9365914.
APA :
Dembri, A. & Gharzouli, M. (2020, May). Graph-based Model for Negative e-WOM Influence in Social Media. In Proceedings of the 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), Marrakeche, Morroco, IEEE, 1-6. DOI: https://doi.org/10.1109/CloudTech49835.2020.9365914
IEEE :
A. Dembri and M. Gharzouli, "Graph-based Model for Negative e-WOM Influence in Social Media". In Proceedings of the 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), Marrakeche, Morroco, IEEE, pp. 1-6, May, 2020. DOI: https://doi.org/10.1109/CloudTech49835.2020.9365914.
BibTeX :
@inproceedings{misc-lab-349,
author = {Dembri, Abderraouf and Gharzouli, Mohamed},
title = {Graph-based Model for Negative e-WOM Influence in Social Media},
booktitle = {5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)},
location = {Marrakeche, Morroco},
isbn = {978-1-7281-6175-4},
pages = {1--6},
publisher = {IEEE},
year = {2020},
month = {May},
doi = {10.1109/CloudTech49835.2020.9365914},
url = {https://ieeexplore.ieee.org/document/9365914},
keywords = {Social media marketing, Electronic word-of-mouth, Social commerce, Machine learning}
}
RIS :
TY  - CONF
TI - Graph-based Model for Negative e-WOM Influence in Social Media
AU - A. Dembri
AU - M. Gharzouli
PY - 2020
BT - 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), Marrakeche, Morroco
SN - 978-1-7281-6175-4
SP - 1
EP - 6
PB - IEEE
AB - Nowadays, several companies use social media marketing to increase profit and control the market. The customer's feedback has a powerful influence on company reputation by conveying their experience in social media. Customers exchange their feedback about the services using electronic Word-of-Mouth (e-WOM). Negative feedback could help companies improve their service to increase profit. In this work, we propose an approach to determine the effect of negative e-WOM relating to a company's products or services. Firstly, we apply a machine-learning algorithm called random forest to classify e-WOM on three classes based on polarity: Positive, negative, or neutral. Secondly, we group negative e-WOM into different clusters based on their topics using a similarity method named cosine similarity. Thirdly, we generate an influence graph of negative e-WOM based on time precedence and social ties. Finally, we analyze the resulted graph to identify risk patterns and convey useful information. The provided method is implemented using Python and is tested with collected data.
KW - Social media marketing
KW - Electronic word-of-mouth
KW - Social commerce
KW - Machine learning
DO - 10.1109/CloudTech49835.2020.9365914
UR - https://ieeexplore.ieee.org/document/9365914
ID - misc-lab-349
ER -