Monir Dahbi
ABSTRACT
Smart cities utilize different devices not only to solve the increasingly serious urban resource shortage, environmental pollution, traffic congestion, security risks but also to identify concerns of citizens. Building a smart city is not free from using social networks that have changed citizen's daily life and becoming a new source of real-time information, so there is no doubt that sentiment analysis can contribute as important decision support. we take these challenges by presenting a set of features that have been used with machine learning techniques, sentiment analysis, text classification to extract the intelligence needed from social media feeds containing Moroccan dialects. A case scenario analyses the opinions of users concerning the traffic in three cities in Morocco is illustrated in the following.
- Ahmed, Kaoutar Ben, et al. "Sentiment Analysis for Smart Cities: State of the Art and Opportunities." Proceedings on the International Conference on Internet Computing (ICOMP). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016.Google Scholar
- Vakali, Athena, Despoina Chatzakou, Vassiliki Koutsonikola, and Georgios Andreadis. "Social data sentiment analysis in smart environments.",2013Google Scholar
- Roberts, Helen, Jon Sadler, and Lee Chapman. "The value of Twitter data for determining the emotional responses of people to urban green spaces: A case study and critical evaluation." Urban Studies 56.4 (2019): 818--835.Google ScholarCross Ref
- Souza, A., Figueredo, M., Cacho, N., Araújo, D., Coelho, J., & Prolo, C. A. (2016, September). Social smart city: A platform to analyze social streams in smart city initiatives. In 2016 IEEE International Smart Cities Conference (ISC2) (pp. 1--6). IEEE.Google ScholarCross Ref
- Li, Mengdi, et al. "The new eye of smart city: novel citizen sentiment analysis in twitter." 2016 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2016.Google Scholar
- Alomari, Khaled Mohammad, Hatem M. ElSherif, and Khaled Shaalan. "Arabic tweets sentimental analysis using machine learning." International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Cham, 2017.Google Scholar
- Duwairi, Rehab M., et al. "Sentiment analysis in arabic tweets." 2014 5th International Conference on Information and Communication Systems (ICICS). IEEE, 2014.Google Scholar
- Duwairi, Rehab M., et al. "Sentiment analysis for Arabizi text." 2016 7th International Conference on Information and Communication Systems (ICICS). IEEE, 2016.Google Scholar
- Assiri, Adel, Ahmed Emam, and Hmood Al-Dossari. "Saudi twitter corpus for sentiment analysis." World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 10.2 (2016): 272--275.Google Scholar
- Aldayel, Haifa K., and Aqil M. Azmi. "Arabic tweets sentiment analysis-a hybrid scheme." Journal of Information Science 42.6 (2016): 782--797.Google ScholarDigital Library
- Guellil, Imane, et al. "Sentialg: Automated corpus annotation for algerian sentiment analysis." International Conference on Brain Inspired Cognitive Systems. Springer, Cham, 2018.Google Scholar
- Hussien, Intisar O., Kia Dashtipour, and Amir Hussain. "Comparison of Sentiment Analysis Approaches Using Modern Arabic and Sudanese Dialect." International Conference on Brain Inspired Cognitive Systems. Springer, Cham, 2018.Google Scholar
- Alahmary, Rahma M., Hmood Z. Al-Dossari, and Ahmed Z. Emam. "Sentiment Analysis of Saudi Dialect Using Deep Learning Techniques." 2019 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2019.Google Scholar
- Alahmary, Rahma M., Hmood Z. Al-Dossari, and Ahmed Z. Emam. "Sentiment Analysis of Saudi Dialect Using Deep Learning Techniques." 2019 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2019.Google Scholar
- ISRI documentation https://www.nltk.org/_modules/nltk/stem/isri.htmlGoogle Scholar
- Scikit-Learn documentation. https://scikit-learn.org/stable/documentation.htmlGoogle Scholar
- Scikit-Learn https://en.wikipedia.org/wiki/Scikit-learnGoogle Scholar
- Scikit-Learn https://jakevdp.github.io/PythonDataScienceHandbook/05.02-introducing-scikit-learn.htmlGoogle Scholar
- Han, J., Kamber, M.: Data mining: concepts and techniques, 2nd ed., [Nachdr.].Google Scholar
- Kruczkowski, Michał & Niewiadomska-Szynkiewicz, Ewa. (2014). Comparative study of supervised learning methods for malware analysis. Journal of Telecommunications and Information Technology. 2014.Google Scholar
- Aldayel, Haifa K., and Aqil M. Azmi. "Arabic tweets sentiment analysis-a hybrid scheme." Journal of Information Science 42.6 (2016): 782--797Google ScholarDigital Library
- Faidi, K., Ayed, R., Bounhas, I., & Elayeb, B. (2014). Comparing Arabic NLP tools for Hadith classification. In Proceedings of the 2nd International Conference on Islamic Applications in Computer Science and Technologies (IMAN'14).Google Scholar
- Chauhan, Priyavrat. (2017). Sentiment Analysis: A Comparative Study of Supervised Machine Learning Algorithms Using Rapid miner. International Journal for Research in Applied Science and Engineering Technology. V. 80--89. Google ScholarCross Ref
Recommendations
Social Media in Smart Cities: An Exploratory Research in Mexican Municipalities
HICSS '15: Proceedings of the 2015 48th Hawaii International Conference on System SciencesSocial media tools have been the main tool in many municipal portals. Municipalities have used Twitter and Facebook platforms to improve communication and speed services provisions as an initial step to become a smart city. The purpose of this research ...
How Social Media Influencers Govern Sentiment Territory
In present research, the authors examined how social media influencers affect the overall sentiment of a topic. To this end, they utilized supervised machine learning approach to develop SentiRobo for measuring the sentiment score of social media ...
Social media mining for smart cities and smart villages research
AbstractThe imperative of well-being and improved quality of life in smart cities context can only be attained if the smart services, so central to the concept of smart cities, correspond with the needs, expectations and skills of cities’ inhabitants. ...
Comments