Evaluating Citizens’ Sentiments in Smart Cities: A  Deep Learning Approach

Elabora, Abdallah; Alkhatib, Manar; Samuel Mathew, Sujith; El Barachi, May

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Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach

Date

2021-08-17

Authors

Elabora, Abdallah

Alkhatib, Manar

Samuel Mathew, Sujith

El Barachi, May

Publisher

IEEE

Abstract

Sentiment analysis of user-generated online content is crucial for smart city analytics and relevant social services. Researchers have relied mainly on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their feelings and share emotions. Sentiment analysis of such large scale visual content, such as those in image tweets, helps to obtain user sentiments toward events or topics and therefore complement textual sentiment analysis. Motivated by the need to leverage large scale yet noisy training data to solve the extremely challenging problem of face sentiment analysis, we employ Convolutional Neural Networks (CNN). We designed a suitable CNN architecture to classify facial emotions and analyze sentiments. We have conducted extensive experiments on labeled images. The results show that the proposed CNN achieved a very good performance in face sentiment analysis with 89.9% of F1-measure

Keywords

Sentiment analysis, Face Recognition, Convolutional Neural Network, Deep Learning

Citation

Elabora, A. et al. (2020) “Evaluating Citizens’ Sentiments in Smart Cities: A Deep Learning Approach,” in 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1–5.