research-article

OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features

Authors:

Wyverson Bonasoli de Oliveira,

Leyza Baldo Dorini,

Rodrigo Minetto,

Thiago H. SilvaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 38, Issue 3

Article No.: 23, Pages 1 - 28

https://doi.org/10.1145/3385186

Published: 21 April 2020 Publication History

Abstract

Opinion mining in outdoor images posted by users during different activities can provide valuable information to better understand urban areas. In this regard, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures and one specifically designed for sentiment analysis. We also evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification and cross-dataset generalization performance. The evaluation explores a novel dataset—namely, OutdoorSent—and other publicly available datasets. We observe that the incorporation of knowledge about semantic attributes improves the accuracy of all ConvNet architectures studied. Besides, we found that exploring only images related to the context of the study—outdoor, in our case—is recommended, i.e., indoor images were not significantly helpful. Furthermore, we demonstrated the applicability of our results in the United States city of Chicago, Illinois, showing that they can help to improve the knowledge of subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment, which are also correlated with median income, opening up opportunities in different fields.

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Kosti MGeorgakopoulou NDiplaris SPistola TChatzistavros KXefteris VTsanousa AVrochidis SKompatsiaris I(2023)Assessing Virtual Reality Spaces for Elders Using Image-Based Sentiment Analysis and Stress Level DetectionSensors10.3390/s2308413023:8(4130)Online publication date: 20-Apr-2023
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Lopes CMinetto RDelgado MSilva T(2023)PerceptSent - Exploring Subjectivity in a Novel Dataset for Visual Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322523814:3(1817-1831)Online publication date: 1-Jul-2023
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Index Terms

OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
  2. Machine learning
2. Information systems
  1. World Wide Web

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 38, Issue 3

July 2020

311 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3394096

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Publication History

Published: 21 April 2020

Accepted: 01 February 2020

Revised: 01 December 2019

Received: 01 June 2019

Published in TOIS Volume 38, Issue 3

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Lopes CMinetto RDelgado MSilva T(2023)PerceptSent - Exploring Subjectivity in a Novel Dataset for Visual Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322523814:3(1817-1831)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TAFFC.2022.3225238
Silver DSilva T(2023)Complex causal structures of neighbourhood change: Evidence from a functionalist model and yelp dataCities10.1016/j.cities.2022.104130133(104130)Online publication date: Mar-2023
https://doi.org/10.1016/j.cities.2022.104130
Chatzistavros KPistola TDiplaris SIoannidis KVrochidis SKompatsiaris I(2022)Sentiment analysis on 2D images of urban and indoor spaces using deep learning architecturesProceedings of the 19th International Conference on Content-based Multimedia Indexing10.1145/3549555.3549575(43-49)Online publication date: 14-Sep-2022
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