nttc 0.2.2

A set of functions that process and create topic models from a sample of community-detected Twitter networks' tweets.

NTTC (Name That Twitter Community!) A Tweets Topic Modeling Processor for Python 3

by Chris Lindgren [e-mail] Distributed under the BSD 3-clause license. See LICENSE.txt or http://opensource.org/licenses/BSD-3-Clause for details.

Overview

A set of functions that process and create topic models from a sample of community-detected Twitter networks' tweets.

It assumes you seek an answer to the following questions:

1. What communities persist or are ephemeral across periods in the copora, and when?
2. What can these communities be named, based on their sources, targets, topics, and top-RT'd tweets?
3. Of these communities, what are their topics over time?

Accordingly, it assumes you have a desire to investigate tweets from each detected community across already defined periodic episodes with the goal of naming each community AND examining their respective topics over time in the corpus.

It functions only with Python 3.x and is not backwards-compatible (although one could probably branch off a 2.x port with minimal effort).

Warning: nttc performs no custom error-handling, so make sure your inputs are formatted properly! If you have questions, please let me know via email.

System requirements

• nltk
• pandas
• numpy
• emoji
• pprint
• gensim
• spacy

Installation

pip install nttc

Functions

nttc contains the following functions:

• get_csv: Loads CSV data as a pandas DataFrame.
• write_csv: Writes DataFrame as a CSV file.
• create_hub_csv_files: Writes all of the objects' source/target information as a CSV of "hubs"
• get_comm_nums: Filters Dataframe column community values into a List.
• get_all_comms: Slice the full set to community and their respective tweets. Arguments: Full dataframe, strings of column names for community and tweets.
• comm_dict_writer: Writes per Community tweets into a dictionary.
• split_community_tweets: Isolates community's tweets, then splits string into list of strings per Tweet preparing them for the topic modeling. Returns as Dataframe of tweets for resepective community.
• clean_split_docs: Removes punctuation, makes lowercase, removes stopwords, and converts into dataframe for topic modeling.
• tm_maker: Creates data for TM and builds an LDA TM.
• get_hubs_top_rts: Appends hubs' top 10 RT'd tweets and usernames to respective period and community object.
  • Args:
    • Dataframe of hub targets,
    • Dict of Objects with .top_rts,
    • String of period number
  • Returns: Dict Object with new .top_rts per Object
• get_hubs_targets: Appends hubs' targets data to respective period and community object.
  • Args:
    • Dataframe of hub targets,
    • Dict of Objects,
    • String of column name for period,
    • String of period number,
    • String of column name for the community number
  • Returns: Dict Object with new .targets per Object
• merge_rts_targets: Merges hubs' sources and targets data as a full list per Community.

Build a topic model per Community and save all variables to respective object properties.

import nttc

data_path = '//Users/name/project/periods/top_rts/encoded'
__file__ = 'p1_comm_top500mentions_in_top10000_rts_count_uid.csv'

dtype_dict={
    'community': str,
    'tweets': str,
    'retweets_count': float,
    'link': str,
    'username': str,
    'user_id': float
}

# 1. Load CSV
df_tweets = nttc.get_csv(data_path, __file__, dtype_dict)
# 2. Get community numbers into a List
comm_list = nttc.get_comm_nums(df_tweets)
# 3. Write dictionary of tweets organized by per Community perspective
dict_all_comms = nttc.comm_dict_writer(comm_list, df_tweets, 'community', 'tweets')
# 4 . Process tweets for each community
split_dict_all_comms = nttc.split_community_tweets(dict_all_comms, 'tweets')
# 5. Build the topic model
tms_full_dict = nttc.tm_maker(random_seed=2018, 
                                                    split_comms=split_dict_all_comms, 
                                                    num_topics=5,
                                                    random_state=100,
                                                    update_every=1,
                                                    chunksize=100,
                                                    passes=10,
                                                    alpha='auto',
                                                    per_word_topics=True) #pass any of the following gensim LDATopicModel() object arguments here

Sample Output from Above Code

3  Perplexity:  -7.618915328673395

 3  Coherence Score:  0.3740323991406477

 5  Perplexity:  -7.749282621692275

 5  Coherence Score:  0.36001967258313305

 6  Perplexity:  -7.475628335657981

 6  Coherence Score:  0.32547481443269244

 7  Perplexity:  -7.264458923588148

 7  Coherence Score:  0.31947706630738704

 8  Perplexity:  -7.839326042415438

 8  Coherence Score:  0.31957579040223866

 9  Perplexity:  -7.670416717009498

 9  Coherence Score:  0.28534510836872357

 10  Perplexity:  -7.370800819131035

 10  Coherence Score:  0.34724361008183413

 12  Perplexity:  -6.9411620263614795

 12  Coherence Score:  0.397521213421681

 17  Perplexity:  -6.068761633181642

 17  Coherence Score:  0.44224500342072987

 27  Perplexity:  -6.345910693707283

 27  Coherence Score:  0.41525260201784386

 Modeling complete.

Add targets to each community's .target property

Merge Top 10 RT and target information to each community's .full_hub property

Using some outputs from each object, you can visualize the topic models

You can also output the entire hub as a CSV for closer analysis

nttc.create_hub_csv_files(
    full_obj=full_obj,
    sys_path=data_path,
    output_file='p1_full_hubs.csv',
    drop_dup_cols=True
)