most citedTopology Analysis of International Networks Based on Debates in the United Nations

7 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

stat.AP20171 cited

Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data

Anastasia Ushakova, Slava J. Mikhaylov

Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows…

cs.CL20176 cited

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Radoslaw Kowalski, Marc Esteve, Slava J. Mikhaylov

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction rem…

cs.CL2017

Data Innovation for International Development: An overview of natural language processing for qualitative data analysis

Philipp Broniecki, Anna Hanchar, Slava J. Mikhaylov

Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both tr…

cs.CL2017

What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016

Alexander Baturo, Niheer Dasandi, Slava J. Mikhaylov

There is surprisingly little known about agenda setting for international development in the United Nations (UN) despite it having a significant influence on the process and outcom…

cs.CL2017

Database of Parliamentary Speeches in Ireland, 1919-2013

Alexander Herzog, Slava J. Mikhaylov

We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dáil Éireann, the lower house and principal chamber of the Irish par…

cs.CL20177 cited

Topology Analysis of International Networks Based on Debates in the United Nations

Stefano Gurciullo, Slava Mikhaylov

In complex, high dimensional and unstructured data it is often difficult to extract meaningful patterns. This is especially the case when dealing with textual data. Recent studies…