Here you can find information about my data science projects

Data Analytics and Natural Language Processing

How does the communication of European central banks evolve? To what issues do they pay attention? All together and individually? I explore the building blocks of central banks' communication by applying topic modeling to the corpus of their annual report from 1999 to 2020.

**Data**: Introductions to annual reports of European central banks from 1999 to 2020 (collected manually from official sites).

**Analysis**: preprocessing (NLTK, spaCy), phrases detection (Gensim), topic modeling (Gensim).

**Visualzation**: [Tableau](https://public.tableau.com/app/profile/arina.tsirkuleva/viz/CBsCommunication/CentralbanksCommunication).

**Description**: I build on a corpus of introductions to
annual reports of European central banks (CBs) that constituted the Eurosystem, the monetary authority of the Eurozone.
Eurosystem comprises the European Central Bank (ECB) and national central banks
(NCBs) that adopted the euro as a single currency. In the analysis, I address the communication of 10 NCBs that launched the euro in 1999\*, and the CB of Greece that joined the Eurosystem in 2001.

I split texts into chunks of comparable size, first
dividing texts into paragraphs and then merging paragraphs that are too small.
After, I train the LDA topic model (***Gensim***
implementation). Topic modeling is an unsupervised clustering algorithm that defines the main themes of a text collection. I fit the model of 25 topics on the whole corpus, and these topics illustrate the focal points
of CBs' attention in the period from 1999 to 2020.

In ***Tableau***, I
visualize topics’ evolution
over years.
The visualization
allows to compare the use of topics by each CB with the annual average of all CBs (upper chart).
As well, I show how the attention
to each topic changes for
every CB (lower
chart).

\*The CB
of Portugal, the 11th
NCB to start the Eurosystem, is excluded from the analysis for the lack of data.


Building blocks of central banks' communication

In a common monetary policy framework, how distant are European national central banks and the ECB, the supranational monetary authority? I explore the proximity of their communication in the period from 1999 to 2020.

**Data**: Introduction to annual reports of European central banks
from 1999 to 2020 (manually collected from officieal sites).

**Analysis**: Hellinger distance applied to topics defined
[previously](https://public.tableau.com/app/profile/arina.tsirkuleva/viz/CBsCommunication/CentralbanksCommunication).

**Visualization**: [Tableau.](https://public.tableau.com/app/profile/arina.tsirkuleva/viz/Centralbanksdistance/Distance_ECB)

**Description**: I build on a corpus of introductions to annual
reports of European central banks (CBs) that constituted the Eurosystem, the monetary authority of the Eurozone. Eurosystem comprises
the European Central Bank (ECB) and national central banks (NCBs) that adopted
the euro as a single currency. In
the analysis, I address the communication of 10 NCBs that launched the euro in 1999\*, and the CB of Greece that joined the Eurosystem in 2001.

I use [previously](https://public.tableau.com/app/profile/arina.tsirkuleva/viz/CBsCommunication/CentralbanksCommunication) defined 25 topics (main themes of a text collection) to
measure pairwise distances between the ECB and national central banks (NCBs). As a result of topic modeling, each
document of the text collection is quantified as a distribution of topics,
i.e., a list combining topic indices and topic shares in the document. Hence, I
apply Hellinger distance metrics on these distributions to measure distances between text pairs
(introductions to annual reports, in my case). Higher values correspond to more distance, and lower – to
more similarity.

I visualize the
results as an interactive dashboard in ***Tableau*** that shows the distance dynamics between individual NCBs and
the ECB.

\* The CB
of Portugal, the 11th
NCB to start the Eurosystem, is excluded from the analysis for the lack of data.


Distance between the ECB and national central banks

How central banks and central bankers are connected? I extract the most mentioned entities in the central banks’ communication from 2007 to 2021 to understand patterns of their interaction and coinfluence.

**Data**: Central
banks’ speeches from the
[BIS site](https://www.bis.org/cbspeeches/index.htm).  Scraped with Selenium (Python) in the end of 2021.

**Time frame**: 2007
– 2021.

**Analysis**: preprocessing (*spaCy*), named-entity recognition (*spaCy*), network creation (*NetworkX*), Louvain method for community detection.

**Visualization**: Pyvis ([organisations](https://arinatsr.github.io/cbs_org/)) and ([persons](https://arinatsr.github.io/cbs_pers/)) \[*suggested to use Force Atlas2 solver*].

**Description**: I build on a
corpus of central bankers’
speeches from 2007 to 2021.

In the analysis, I use ***spaCy*** to parse named entities (i.e.,
real-world objects, such as organizations, people, and geographic locations). In
particular, I focus on organizations and persons to understand which
entities/personalities central banks refer to the most in their communication. As a result of parsing, I get
a table that connects a central bank that delivers communication (source
column) with organizations/personalities that are mentioned in this particular
communication (target column). I exclude all self-references, automatically
(using [ECB
abbreviations glossary](https://www.ecb.europa.eu/services/glossary/html/glossa.en.html)) and manually resolve the
most frequent coreferences (i.e., different spellings of the same entity, such
as “us federal reserve”, “federal reserve system”, “fed”), and apply a
threshold of 10 word occurrences.

Using the source-target table, I generate two networks in ***NetworkX*** and visualize
them as interactive graphs with ***Pyvis***,
one for organizations and another for personalities.


Home

Data Analytics and Natural Language Processing

Building blocks of central banks' communication

Distance between the ECB and national central banks

Global network of central banks and central bankers