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Public defence in computer and information science: Kostiantyn Kucher

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Published: Tuesday, 19 March 2019 12:46

Kostiantyn Kucher is going to present his doctoral dissertation, which is mainly based on the results of the StaViCTA project in the area of information visualization and visual analytics, on April 15, 2019.

 

Title: Sentiment and stance visualization of textual data for social media
Subject: Computer and information science
Faculty: Faculty of technology
Date: Monday 15 April 2019 at 9.15 am
Place: Room Wicksell, building K, Växjö
External reviewer: Associate professor Ross Maciejewski, Arizona State University, USA
Examining committee: Professor Jarke van Wijk, Eindhoven University of Technology, the Netherlands; professor Jörn Kohlhammer, Technische Universität Darmstadt, Germany; professor Gerik Scheuermann, Leipzig University, Germany
Chairperson: Associate professor Morgan Ericsson, Department of computer science and media technology, Linnaeus University
Supervisor: Professor Andreas Kerren, Department of computer science and media technology, Linnaeus University
Examiner: Professor Welf Löwe, Department of computer science and media technology, Linnaeus University
Spikning: Friday 15 March 2019 at 11.00 am at the University Library in Växjö

 

Dissertation is available from DiVA: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-81081 

More information at the LNU website: https://lnu.se/en/meet-linnaeus-university/current/events/2019/public-defence-kostiantyn-kucher-190415/  +  https://lnu.se/en/meet-linnaeus-university/current/news/2019/new-research-helps-visualise-sentiment-and-stance-in-social-media/

Special Issue on Natural Language Processing for Social Media Analysis

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Published: Thursday, 21 June 2018 11:26

Call for Papers
International Journal on Artificial Intelligence Tools
Special Issue on Natural Language Processing for Social Media Analysis (NLP4SMA)

https://www.worldscientific.com/page/ijait/callforpapers02

Guest Editors:
Iosif Mporas, This email address is being protected from spambots. You need JavaScript enabled to view it.
Vasiliki Simaki, This email address is being protected from spambots. You need JavaScript enabled to view it.
Carita Paradis, This email address is being protected from spambots. You need JavaScript enabled to view it.
Andreas Kerren, This email address is being protected from spambots. You need JavaScript enabled to view it.
Michael Paraskevas, This email address is being protected from spambots. You need JavaScript enabled to view it.

The world has witnessed the power of digitized information dissemination via different social media channels, facilitating the spread and organization of information. More and more people use social media on a daily basis in order to express opinions about various topics such as politics, music, lifestyle, environment, or personal matters. This activity produces a massive number of audio-visual data, and text data that need to be further analyzed according to different criteria set in each case.
Text data from social media can provide important information about social media users, their identity and preferences, habits, topics discussed and the trends they follow. Deriving such information from social media text is an intriguing task in the fields of text mining, information visualization and natural language processing (NLP) with a wide range of applications in the social sciences and social media analytics.
In this special issue, we invite research papers in the broader field of NLP techniques for social media text analysis. The topics of interest include (but are not limited to):

Social media text processing for
• Sentiment analysis
• Stance detection
• Trend analysis
• Topic modelling
• Gender, age, personality identification
• Native/non-native language identification
• Language variety identification
• Mental health modelling
• Dementia and frailty identification
• Visualization of social media text
• Knowledge discovery from social media text

Paper Submission Deadline: January 18, 2019
First Decision Notification: March 31, 2019
Revised Paper Submission: May 30, 2019
Final Paper Submission: June 30, 2019
Final Decision Notification: July 30, 2019

Submissions will be made through IJAIT "Online Submission'" System at http://www.editorialmanager.com/ijait/login.asp. Please submit your paper under the NLP4SMA folder.

Blog Entry on Medium

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Published: Friday, 25 May 2018 18:27

The ACM TiiS journal invited us to write a blog entry on our article on ALVA. It has been published recently on Medium. 

Popular Scientific Summary of StaViCTA

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Published: Thursday, 22 March 2018 09:29

The StaViCTA project focused on the analysis of stance in English written online social media, such as blog or Twitter posts. Taking stance is the expression of attitudes, judgments, doubts, trust, or certainty about a specific topic, and its analysis is crucial for application fields like crisis management, financial analytics, or business intelligence. Our research interests were to identify the language resources for such expressions in the context of social media and how they act together over time. In addition, we wanted to research the technologies that are needed to achieve these goals. Consequently, the members of StaViCTA came from different subjects—linguistics, computational linguistics, and information visualization—in order to exploit synergies and to enable human beings to make sense of large dynamic text data and allow for exploration, control and final evaluation of the analysis processes and results.

To increase the chances of finding enough stance expressions in social media, we concentrated on political blogs, for example on the Brexit. The ten stance categories chosen are AGREEMENT/DISAGREEMENT, CERTAINTY, CONTRARIETY, HYPOTHETICALITY, NECESSITY, PREDICTION, TACT/RUDENESS, SOURCE OF KNOWLEDGE, UNCERTAINTY, and VOLITION. From this, we compiled a gold standard stance corpus on which we then carried out further analyses, for instance, which of these stance categories co-occur together and which not. This corpus has been made available publicly. We then attested that our notional approach was successful in identifying stance-taking in discourse.

On the computational side, we developed so-called machine learning classifiers that are specialized on political texts and able to identify the above-mentioned categories in social media texts. We applied a machine learning technique called active learning for the automatic selection of useful training samples and for subsequent interactive querying of a person to manually provide the right classification. Here, we showed the usefulness of a number of methods, which optimize for resource-efficiency when collecting training data, implemented them, and made them freely available via SND.

Interactive visualization helps to bring all these concepts together and provides the users with a tool to effectively access the textual online data, to apply and interpret the classifiers and their results, but also to make the process of building the training data for the classifiers more efficient and analyzable. Thus, we developed a number of novel, web-based visualization approaches for investigating lexical features for stance phenomena in social media and for supporting text data annotation and classifier training by using active learning stance classification. Finally, we implemented visualization tools for specific application areas, such as digital humanities, that built on our project results.

Seminar at Lund University

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Published: Wednesday, 13 December 2017 12:23

On November 8th 2017, Vasiliki Simaki gave a seminar within the English Linguistics Seminars organized by the English Studies Department, Centre for Languages and Literature, Lund University. The title of the seminar was: "StaViCTA interdisciplinary project: linguistic insights of en-stanced discourse".

In this seminar, the linguistic resource created within StaViCTA, the Brexit Blog Corpus (BBC), was presented. The development of the StaViCTA novel stance framework, based on notional stance categories was explained, and the collection and annotation of text data was described. The statistical, computational and qualitative analysis of the BBC, as well as the findings about the en-stanced discourse  of political blogs were analyzed and discussed.