The State of the Art in Sentiment Visualization

Kucher, Kostiantyn; Paradis, Carita; Kerren, Andreas

doi:10.1111/cgf.13217

Cited by 83 publications

(66 citation statements)

References 151 publications

(277 reference statements)

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“…Social media sites are rich sources of information that enable researchers to perform different kinds of analysis, including event analysis and cross-platform information linking [2,5,8,23], visual sentiment analysis [20], opinion diffusion analysis [6,43,46], and text analysis and topic modeling [15,25]. A thorough review of visual analytics research using social media data is presented in [7,42].…”

Section: Visual Social Media Analyticsmentioning

confidence: 99%

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

Khayat

Karimzadeh

Zhao

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.

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Section: Visual Social Media Analyticsmentioning

confidence: 99%

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

Khayat

Karimzadeh

Zhao

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…introduced the categorization for sentiment visualization techniques [9]. Siersdorfer et al (2010) presented analyzing and predicting the sentiment of images on the social web.…”

Section: International Journal Of Computer and Communication Engineeringmentioning

confidence: 99%

Research of Security Protocol and Data Compression Method for In-vehicle FlexRay Network

Xu¹,

Liu²,

Wang³

et al. 2020

IJCCE

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Sentiment analysis systems can handle social media images by interpreting the embedded emotional responses in those images. This represents an interesting and challenging problem that tries to figure out the high-level content of large-scale visual data based on algorithms devised from computer vision. This paper presents a system to analyze social media images and visualize the implied emotions from each image as (Happy, Sad, and Neutral). The objective of this work is to introduce a system model with features extraction basis utilizing some adequate technique of machine learning. The applied methodology is pivoted on implementing the required system through several steps of processing. This involves social media image displaying and video frames grabbing, image features extraction, then embedded emotions patterns classification and recognition utilizing a proper convolutional neural network (CNN). Flickr and Twitter datasets were utilized while the pertinent algorithm was developed using "Matlab2017b" platform. This can help social media users visualizing their interests besides forming a better scope of visualization. It will further assist companies in envisaging the mood of users/costumers towards their stock prices in order to set competitive prices for both sides. We design a Deep Attention Network Mechanisms (DANM) to achieve a higher level of social media sentiment image analysis and classify them as (Highly positive mood and highly negative mood). The DANM produces features maps basis utilizing the adequate focusing technique of machine learning based on a proper convolutional neural network (CNN). The proposed CNN training system has proven better results with respect to accuracy and efficiency in comparison with some other similar works. When experimentations on both real and synthetic datasets were conducted, the system showed a percentile improvement of about 14.2%. This system is applicable to a broad horizon of applications such as studying the emotional response of humans on visual stimuli, visual sentiment analysis algorithms and modeling, building machine learning-based robust visual sentiment classifier, as well as in most online websites that involve visual data mining for business intelligence, e-commerce, stock market prediction, political vote forecasts, and video gaming. Communication Engineering analyze the facial actions in any case of context, culture, gender, and so on. The accomplishments in related areas such as psychological studies, human movement analysis, face detection, face tracking, and face recognition make the automatic facial expression analysis possible. It can be applied in many areas such as emotion and paralinguistic communication, clinical psychology, psychiatry, neurology, pain assessment, lie detection, intelligent environments, and multimodal human-computer interface (HCI).This paper aims to articulate the significant sides of implementing an efficient-accurate system for image sentiment analysis and visualization based on the concepts of utilizing Attention convol...

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“…Recent approaches additionally extract emotions and sentiments from (narrative) texts via natural language processing and visualize them [84,85].…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Once upon a Spacetime: Visual Storytelling in Cognitive and Geotemporal Information Spaces

Mayr

Windhager

2018

IJGI

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Stories are an essential mode, not only of human communication-but also of thinking. This paper reflects on the internalization of stories from a cognitive perspective and outlines a visualization framework for supporting the analysis of narrative geotemporal data. We discuss the strengths and limitations of standard techniques for representing spatiotemporal data (coordinated views, animation or slideshow, layer superimposition, juxtaposition, and space-time cube representation) and think about their effects on mental representations of a story. Many current visualization systems offer multiple views and allow the user to investigate different aspects of a story. From a cognitive point of view, it is important to assist users in reconnecting these multiple perspectives into a coherent picture-e.g., by utilizing coherence techniques like seamless transitions. A case study involving visualizing biographical narratives illustrates how the design of advanced visualization systems can be cognitively and conceptually grounded to support the construction of an integrated internal representation.

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The State of the Art in Sentiment Visualization

Abstract: Visualization of sentiments and opinions extracted from or annotated in texts has

Cited by 83 publications

References 151 publications

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

Research of Security Protocol and Data Compression Method for In-vehicle FlexRay Network

Once upon a Spacetime: Visual Storytelling in Cognitive and Geotemporal Information Spaces

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