Sense-Making of Critical Incidents with Sentiment Analysis and Data Visualization

Posts with defamatory content or hate speech are constantly foundon social media. The results for readers are numerous, not restrictedonly to the psychological impact, but also to the growth of thissocial phenomenon. With the General Law on the Protection ofPersonal Data and the Marco Civil da Internet, service providersbecame responsible for the content in their platforms. Consideringthe importance of this issue, this paper aims to analyze the contentpublished (news and comments) on the G1 News Portal with techniquesbased on data visualization and Natural Language Processing,such as sentiment analysis and topic modeling. The results showthat even with most of the comments being neutral or negative andclassified or not as hate speech, the majority of them were acceptedby the users.

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Public Opinion Detection in an Online Lending Forum: Sentiment Analysis and Data Visualization

2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) ◽

10.1109/icccbda49378.2020.9095690 ◽

2020 ◽

Author(s):

Ge Zhan ◽

Ming Wang ◽

Meiyi Zhan

Keyword(s):

Public Opinion ◽

Sentiment Analysis ◽

Data Visualization ◽

Online Lending

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An Enhanced Approach for Using Data Visualization for Sentiment Analysis and Auto Summarization Data

Modern Applied Science ◽

10.5539/mas.v12n9p190 ◽

2018 ◽

Vol 12 (9) ◽

pp. 190

Author(s):

Osama Mohammad Rababah ◽

Esra F. Alzaghoul ◽

Hussam N. Fakhouri

Keyword(s):

Sentiment Analysis ◽

Data Visualization ◽

Full Text ◽

Visual Representation ◽

The Other ◽

The Internet ◽

Original Text ◽

Text Data ◽

Data Summarization ◽

Using Data

With the rapid increase in the size of the data over the internet there is a need for new studies for text data summarization and representation; rather than storing the full text or reading the full text we can store and read a summary that represent the original text. Furthermore, there is a need also to represent the summarized text with visual representation; one picture worth ten thousandwords. In this paper we propose an approach for visual representation of the summarized text;visual resources give creative control over how message is perceived andprovide a faster way to know what where the text about.This approach were implemented and tested on a sample of two datasets one of 50 texts and the other dataset of 80 positive and negative movie comments, the evaluation has been done visually and the percent of success cases has been reported, the precision and recall has been calculated.

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AthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter

Information ◽

10.3390/info12080312 ◽

2021 ◽

Vol 12 (8) ◽

pp. 312

Author(s):

Alexandros Britzolakis ◽

Haridimos Kondylakis ◽

Nikolaos Papadakis

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Real Time ◽

Data Visualization ◽

Visualization Tool ◽

Political Leaders ◽

Political Event ◽

Data Analyst ◽

Industrial Sectors ◽

Social Media Platforms

Sentiment Analysis is an actively growing field with demand in both scientific and industrial sectors. Political sentiment analysis is used when a data analyst wants to determine the opinion of different users on social media platforms regarding a politician or a political event. This paper presents Athena Political Popularity Analysis (AthPPA), a tool for identifying political popularity over Twitter. AthPPA is able to collect in-real-time tweets and for each tweet to extract metadata such as number of likes, retweets per tweet etc. Then it processes their text in order to calculate their overall sentiment. For the calculation of sentiment analysis, we have implemented a sentiment analyzer that is able to identify the grammatical issues of a sentence as well as a lexicon of negative and positive words designed specifically for political sentiment analysis. An analytic engine processes the collected data and provides different visualizations that provide additional insights on the collected data. We show how we applied our framework to the three most prominent Greek political leaders in Greece and present our findings there.

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Twitter Data Visualization and Sentiment Analysis of Article 370

2019 International Conference on Advances in Computing, Communication and Control (ICAC3) ◽

10.1109/icac347590.2019.9036800 ◽

2019 ◽

Author(s):

Rupali Patil ◽

Nishant Gada ◽

Krisha Gala

Keyword(s):

Sentiment Analysis ◽

Data Visualization ◽

Twitter Data

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Supervised Sentiment Analysis of Twitter Handle of President Trump with Data Visualization Technique

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) ◽

10.1109/ccwc47524.2020.9031237 ◽

2020 ◽

Author(s):

Kalyan Sahu ◽

Yu Bai ◽

Yoonsuk Choi

Keyword(s):

Sentiment Analysis ◽

Data Visualization ◽

Visualization Technique

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Does Visualization Matter? The Role of Interactive Data Visualization to Make Sense of Information

Australasian Journal of Information Systems ◽

10.3127/ajis.v22i0.1681 ◽

2018 ◽

Vol 22 ◽

Cited By ~ 1

Author(s):

Arif Perdana ◽

Alastair Rob ◽

Fiona Rohde

Keyword(s):

Decision Making ◽

Data Visualization ◽

Financial Statement ◽

Sense Making ◽

Business Analytics ◽

Theoretical Understanding ◽

Nonprofessional Investors ◽

Information Reporting ◽

Interactive Data

As part of business analytics (BA) technologies, reporting and visualization play essential roles in mitigating users’ limitations (i.e., being inexperienced, having limited knowledge, and relying on simplified information). Reporting and visualization can potentially enhance users’ sense-making, thus permitting them to focus more on the information’s message rather than numerical analysis. To better understand the role of reporting and visualization in a contextualized environment, we investigate using interactive data visualization (IDV) within accounting. We aim to understand whether IDV can help enhance non-professional investors’ ability to make sense of foundational financial statement analyses. This study conducted an experiment using a sample of 324 nonprofessional investors. Our findings indicate that nonprofessional investors who use IDV are more heuristically adept than non-professional investors who use non-IDV. These findings enrich the theoretical understanding of business analytics’ use in accounting decision making. The results of this study also suggest several practical courses of action, such as promoting wider use of IDV and making affordable IDV more broadly available, particularly for non-professional investors.

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