Estimate Sentiment of Crowds from Social Media during City Events

Gong, Vincent X.; Daamen, Winnie; Bozzon, Alessandro; Hoogendoorn, Serge P.

doi:10.1177/0361198119846461

Cited by 12 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several techniques for estimating feelings in the opinions (Gong, Daamen, Bozzon, & Hoogendoorn, 2019). The CSR‐IRIS method is based in the Naïve Bayes algorithm , which is the most used machine learning algorithm for this purpose and has proven to give good results for sentiment analysis on Twitter (Kumar Ravindran & Garg, 2015).…”

Section: Proposed Methods To Assess the Csr Of A Company Using Informamentioning

confidence: 99%

Using big data to evaluate corporate social responsibility and sustainable development practices

Barbeito-Caamaño

Chalmeta

2020

Corp Soc Responsibility Env

View full text Add to dashboard Cite

This paper shows an alternative method to evaluate sustainable development and corporate social responsibility (CSR) practices based on the opinions of companies' stakeholders expressed on Twitter. An application of the method is performed with the Inditex textile group. This paper shows two important findings: (a) knowledge about companies' CSR practices and stakeholders opinions can be obtained using big data to analyse CSR information about the company on online social networks; and (b) there are important differences between the contents of the sustainability report of a company and the CSR opinions of its stakeholders on the internet. These findings have benefits for the company's stakeholders, who will be able to know the CSR practices of a company in a more objective way, and for the company, which will be able to improve its CSR performance and communication strategy, as well as the stakeholder engagement.

show abstract

Section: Proposed Methods To Assess the Csr Of A Company Using Informamentioning

confidence: 99%

Using big data to evaluate corporate social responsibility and sustainable development practices

Barbeito-Caamaño

Chalmeta

2020

Corp Soc Responsibility Env

View full text Add to dashboard Cite

show abstract

“…[28] and [29] look at sporting events as well as other events that gather masses, such as riots. [30] investigate different city events, ranging from riots to celebrations. The number of people ranges from smaller gatherings (in specific locations) to mass gatherings (e.g.…”

Section: Group Typesmentioning

confidence: 99%

“…The high number of studies using this dimension can be attributed to a highly popular image dataset using it (GAFF, see Section 4). Two studies that use the valence dimension but do not work with this dataset, are [30] and [25]. For a list of all other studies using this categorisation, see Table 9.…”

Section: Emotion Modelsmentioning

confidence: 99%

“…Ngo et al [4] employ the NRC hashtag emotion lexicon [56], which is labelled with anger, fear, anticipation, trust, surprise, sadness, joy, and disgust. In [30], three different datasets are employed. The first is the University of Michigan Sentiment Analysis competition dataset, containing social media posts that are labelled as either negative or positive [57].…”

Section: Other Data Modalitiesmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

show abstract

“…A higher level-of-service of the event area indicates lower density of people in that area, which is safer than the lower level-of-service that refers to high density of people. Using such information together with a set of other qualitative and quantitative interpretation of the crowd, such as sentiment (Gong et al 2019) and composition (Gong et al 2018a), stakeholders apply predefined measures to manage the crowd. The level-ofservice information can be inferred from density of people in that area, which can be further calculated using the number of people in the area and the area of the event place.…”

Section: Introductionmentioning

confidence: 99%

Counting people in the crowd using social media images for crowd management in city events

et al. 2021

View full text Add to dashboard Cite

City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

show abstract

Estimate Sentiment of Crowds from Social Media during City Events

Cited by 12 publications

References 39 publications

Using big data to evaluate corporate social responsibility and sustainable development practices

Using big data to evaluate corporate social responsibility and sustainable development practices

Automatic Emotion Recognition for Groups: A Review

Counting people in the crowd using social media images for crowd management in city events

Contact Info

Product

Resources

About