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twitter-spatial-analysis

=========

Run basic filter, conversion, slicing, and spatial analysis on Twitter data.

src

file name Description
filter_japanese_text.py Set of Mecab filters
filter_text_nightley.py Filter
filter_text_twitter.py Filter for typical tweet database
convert_points_to_grid.py Define spatiotemporal index for experiment. Slice tweets or gps to create density file
convert_tweet_to_gensim.py Slice tweets and convert to text files for gensim
run_gensim_topicmodels.py Run gensim topic modeling models to sliced tweets
settings.py Model and experiment settings

Requirements:

  • Python 3.4 or later
  • PyMySQL
  • MySQL
  • config.cfg
  • MeCab

You'll need a configuration file with twitter API authentication and MySQL connection information. As specified on line 18, make a configuration file "config.cfg" in parent directory. It's a text file. in it, write your twitter API keys and MySQL connection file like below (replace * with your keys).

[twitter]
consumer_key = ****
consumer_secret = ****
access_token_key = ****
access_token_secret = ****

[remote_db]
host = ****
user = ****
passwd = ****
db_name = ****

Workflow:

  1. Check and adjust settings.py Set model parameters, resource paths.

  2. Run filter_text_twitter.py This will apply filter for word segmentation.

  3. Run run_gensim_topicmodels.py This will run topic modeling