Modelling News Sentiment Flow Using Spatial Hawkes Processes: Dependencies Between Topics and Countries
(2018) In Master's Theses in Mathematical Sciences FMS820 20181Mathematical Statistics
- Abstract
- This Master Thesis proposes to use a Spatial Hawkes Process to model the news
flow in the world. The spatial part is divided in both geographical areas as well
as different topics. Therefore, in the Hawkes model every news article corresponds
to a point in space and time. In a certain region and topic the intensity of the
released articles will be modelled with the Hawkes Process. This can be of interest
for various applications depending on the topic and region chosen. The data in this
project comes from the company RavenPack which has labelled every news article
with a topic and a region. The area specifically examined in this report will be the
relations between the different areas in the news flow. A comparison will also be
... (More) - This Master Thesis proposes to use a Spatial Hawkes Process to model the news
flow in the world. The spatial part is divided in both geographical areas as well
as different topics. Therefore, in the Hawkes model every news article corresponds
to a point in space and time. In a certain region and topic the intensity of the
released articles will be modelled with the Hawkes Process. This can be of interest
for various applications depending on the topic and region chosen. The data in this
project comes from the company RavenPack which has labelled every news article
with a topic and a region. The area specifically examined in this report will be the
relations between the different areas in the news flow. A comparison will also be
done between some different spatial divisions to see if different behaviour can be
captured with a more complex model, with more regions and topics.
The model is compared to a Poisson Process model. It seems that the Hawkes
model works better than the Poisson Process to model the intensity of the different
parts of the news flow in all cases. The results also indicates that a very flexible
model will be able to capture more cases that are known from history. The complex
model sees connections that increase the intensity during hectic times in the recent
past news flow, for example during Brexit and the Arab Spring. However it seems
that with more regions the model is prone to overfit and a simpler model may be
preferable for out of sample uses (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8952220
- author
- Brishammar, Carl
- supervisor
- organization
- course
- FMS820 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMS-3353-2018
- ISSN
- 1404-6342
- other publication id
- 2018:E52
- language
- English
- id
- 8952220
- date added to LUP
- 2018-06-21 13:23:51
- date last changed
- 2024-09-19 14:20:46
@misc{8952220, abstract = {{This Master Thesis proposes to use a Spatial Hawkes Process to model the news flow in the world. The spatial part is divided in both geographical areas as well as different topics. Therefore, in the Hawkes model every news article corresponds to a point in space and time. In a certain region and topic the intensity of the released articles will be modelled with the Hawkes Process. This can be of interest for various applications depending on the topic and region chosen. The data in this project comes from the company RavenPack which has labelled every news article with a topic and a region. The area specifically examined in this report will be the relations between the different areas in the news flow. A comparison will also be done between some different spatial divisions to see if different behaviour can be captured with a more complex model, with more regions and topics. The model is compared to a Poisson Process model. It seems that the Hawkes model works better than the Poisson Process to model the intensity of the different parts of the news flow in all cases. The results also indicates that a very flexible model will be able to capture more cases that are known from history. The complex model sees connections that increase the intensity during hectic times in the recent past news flow, for example during Brexit and the Arab Spring. However it seems that with more regions the model is prone to overfit and a simpler model may be preferable for out of sample uses}}, author = {{Brishammar, Carl}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Modelling News Sentiment Flow Using Spatial Hawkes Processes: Dependencies Between Topics and Countries}}, year = {{2018}}, }