near/berliner-winter
1
0
Fork 0
mirror of https://github.com/heyarne/berliner-winter.git synced 2026-05-06 19:23:39 +02:00
Code Issues Projects Releases Wiki Activity

doc

Browse source
This commit is contained in:
Joshua Widmann 2015-02-19 20:35:42 +01:00
commit 7758bd5e26
1 changed files with 1 additions and 1 deletions
Download patch file Download diff file Expand all files Collapse all files

2
README.md
View file

@ -33,7 +33,7 @@ To visualize the incidents on a map, the district on its own seemed to be an imp
Doing this we realized that some of the words we extracted from the text were actually completely irrelevant (*hair*, *face*, *woman*, *evening* and the like). We supposed to be able to identify these irrelevant ones by simply querying a german dictionary whether it contains this word or not, to tell whether this word is a relevant location or just an irrelevant noun from the german language. We ended up checking a text file, containing around 24,000 german nouns. Doing this we realized that some of the words we extracted from the text were actually completely irrelevant (*hair*, *face*, *woman*, *evening* and the like). We supposed to be able to identify these irrelevant ones by simply querying a german dictionary whether it contains this word or not, to tell whether this word is a relevant location or just an irrelevant noun from the german language. We ended up checking a text file, containing around 24,000 german nouns.
## Categorizing incidents ## Categorizing incidents
As we examined some of the incident descriptions we came to the conclusion that it is possible to group most of the incidents to certain categories. We recognized four major categories: *homophic* incidents, *antisemtitic* incidents, *sexist* incidents and *racist* incidents. To automatically assign distinctive categories to each incident we implemented a simple algorithm which searches for certain keywords in the incident description. This way an incidents can be tagged with none or multiple categories ([analyze.py](analyze.py)). We stored these assignements in a seperate table of our SQLite database called *category* with the columns *ID*, *Name* and *Article_ID*. As we examined some of the incident descriptions we came to the conclusion that it is possible to group most of the incidents by certain categories. We recognized four major categories: *homophic* incidents, *antisemtitic* incidents, *sexist* incidents and *racist* incidents. To automatically assign distinctive categories to each incident we implemented a simple algorithm which searches for certain keywords in the incident description. This way an incidents can be tagged with none or multiple categories ([analyze.py](analyze.py)). We stored these assignements in a seperate table of our SQLite database called *category* with the columns *ID*, *Name* and *Article_ID*.
``` ```
bad_words = { bad_words = {