Research on map emotional semantics using deep learning approach

ABSTRACT

The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules.

Key policy highlights

• A Map Sentiment Dataset with five discrete emotions is constructed

• Map emotional semantics are classified by deep learning approaches

• Visual variables Influencing map sentiment are analyzed.

KEYWORDS:

• Image semantics
• convolutional neural networks
• cartography
• feature extraction
• image classification

Acknowledgements

The authors would like to thank every volunteer who participated in the experiment. We are grateful to the anonymous reviewers and the editors of the journal for their important contributions in improving the article for publication.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available at https://doi.org/10.6084/m9.figshare.19766017.v2

URI: https://figshare.com/articles/online_resource/MapSentimentData/19766017

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15230406.2023.2172081.

Additional information

Funding

This research was financially supported by the National Natural Science Foundation of China; under grant [number 42171438]