fb-convos

About

06/03/2016:

Made graphs a little bit more prettier, also added the option to display stats from users on the graph (not shown in pic)

Able to generate basic pie charts to demonstrate the chat distribution across the days

05/03/2016:

Able to generate basic graphs that depict the messaging frequency over time

26/02/2016:

Able to generate wordcloud with masks, simple by adding the -m flag

To use custom masks, replace the mask_color.png in the breakdown folder to the image of your choice

25/02/2016:

Able to extract conversations between two people

Able to generate a wordcloud

Analayze and visualize your facebook converstaions between you and someone!

Todo

1. Who initiates conversation
2. Mouse click events for the graphs

Getting started

Dependencies

matplotlib, wordcloud, ggplot is currently used to aid visualization. To install them, simple execute

pip install matplotlib
pip install wordcloud
pip install seaborn

Step 1 (Setup):

Get a copy of the repository and put the messages.htm that you downloaded from facebook into fb-convos/data/messages.htm. For more info on downloading your facebook data goto https://www.facebook.com/help/131112897028467/

git clone https://github.com/kendricktan/fb-convos.git
cd fb-convos
cp [your messages.htm location here] data/messages.htm

Step 2 (Formatting):

Before doing anything we first need to extract information from the downloaded message data dump (aka messages.htm)

python breakdown/extract.py data/messages.htm data/out.csv [User1's name] [User2's name]

If you want a simple wordcloud generated whilst formatting it, simply add the -wc flag

python breakdown/extract.py -wc data/messages.htm data/out.csv [User1's name] [User2's name]

For more information simply invoke the --help flag

python breakdown/extract.py --help

Step 3 (Wordcloud):

To generate a wordcloud from add the formatted data, simply invoke

python breakdown/analyze-wordcloud.py data/out.csv

For wordcloud which combines messages from both users simply invoke the -ob (output both) flag

python breakdown/analyze-wordcloud.py -ob data/out.csv

To specify a specific year (for example, 2014) do:

python breakdown/analyze-wordcloud.py -y 2014 data/out.csv 

To generate a masked wordcloud (with the default image of a cat), run:

python breakdown/analyze-wordcloud.py -m data/out.csv 

To save the files and don't display the masked wordclouds, run:

python breakdown/anaylze-wordcloud.py -m -dd -s [FOLDER LOCATION] data/out.csv

Again, for more information invoke the --help flag

python breakdown/analyze-wordcloud.py --help

Step 4 (message frequency against time in a graph form)

To generate a graph of the message frequency against time, simply invoke

python breakdown/analyze-msg-freq-grah.py data/out.csv

To specify a start date for the graph (forcefully start displaying from that date), simply invoke the -sd (start date) flag

the -sd flag format is [YYYY-MM-DD]

python breakdown/analyze-msg-freq-grah.py -sd 2015-09-20 data/out.csv

To specify a end date for the graph (forcefully display until that date), simply invole the -ed (end date) flag

the -ed flag format is [YYYY-MM-DD]

python breakdown/analyze-msg-freq-grah.py -sd 2015-09-20 -ed 2016-01-01 data/out.csv

To display all graphs (a.k.a graphs from user1 and user2), add the -da flag

python breakdown/analyze-msg-freq-grah.py -da -sd 2015-09-20 -ed 2016-01-01 data/out.csv

To manually specify a user's name user the -u1 or -u2 command (for the -da output), e.g.

python breakdown/analyze-msg-freq-grah.py -u1 USER1 -u2 USER2 -sd 2015-09-20 -ed 2016-01-01 data/out.csv

Step 5 (chat distribution in a pie chart)

To generate a pie chart of the distribution of chat across the days, simply invoke

python breakdown/analyze-msg-daily-piechart.py data/out.csv