Kazakhstani news corpus for social significance identification with topic modelling results

The presented news corpora consists of 1142735 documents from open Kazakhstani news media and from governmental development programs. The dataset is presented in a form of zip archive containing 12CSV (comma-separated values) files with the dataset split into 100 000 documents in each file.
Each document (row) consists of the following fields:
ID
Title
Text
Source
URL
Datetime
Number of views
90 columns with hand-picked and topic groups weights with semantic names (group_economy, group_politics, etc.). They were normalized to range from 0 to 1
200 columns with topic weights obtained through topic modelling. These columns represent a theta-matrix of the topic model

topic-words.json file represents words with weights for the 200 topics obtained through topic-modellig. It is a compressed representation of a phi matrix

topic-expert-labelling-sentiment.json contains expert labelling of topics sentiment. It was used to obtain results described in the cited article.