top_emojis_Me: thinking face, face with rolling eyes, person shrugging: dark skin tone, person shrugging, pensive face, weary face, face with tears of joy, smiling face with smiling eyes, hugging face,eyes
top_emojis_Friend: weary face, face with tears of joy, face with rolling eyes, expressionless face, thinking face, person tipping hand: medium-dark skin tone, skull, person tipping hand, OK hand: medium-dark skin tone, hugging face
understand social dynamics in a two-way conversation, have hard data to back up your intuition about your relationships.
For more details on the projects, check out the wiki pages.
obtain your text conversations as .txt by running the shell script in this project, titled Baskup . If you want to access the visualization (you do) then you will need bokeh and its dependencies:
- NumPy
- Jinja2
- Six
- Requests
- Tornado >= 4.0
- PyYaml
- DateUtil
- Bokeh
This is best done if you have conda installed and can be done with the simple command:
conda install bokeh
s1 & s2 denote different conversations participants. For more in depth explanations of the calculations & assumptions, see the wiki pages.
# response rates are in seconds
# the first is a median, the other is an average
# response_rate_s1:
# if s1 sends a text, then 22.0 seconds is the most common time
# that they will wait before receiving a reply from s2
# double text & laugh & curse & emoji & link rates are percentage of texts sent
# longest streak is consecutive days talking
# longest drought is consecutive days no talking
# average length is number of words
master_metrics = {
'texts_sent_s1':2453,
'texts_sent_s2':2638,
'response_rate_s1':22.0,
'response_rate_s2':32.0,
'response_rate_mean_s1':2439.84,
'response_rate_mean_s2':1443.09,
'double_text_rate_s1':7.17,
'double_text_rate_s2':13.68,
'emoji_rate_s1':5.055,
'emoji_rate_s2':3.56,
'average_length_s1':11.06,
'average_length_s2':8.97,
'top_emojis_s1':[u'thinking face', u'face with rolling eyes',
u'person shrugging: dark skin tone', u'person shrugging',
u'pensive face', u'weary face', u'face with tears of joy',
u'smiling face with smiling eyes', u'hugging face', u'eyes'],
'top_emojis_s2':[u'weary face', u'face with tears of joy',
u'face with rolling eyes', u'expressionless face',
u'thinking face', u'person tipping hand: medium-dark skin tone',
u'skull', u'person tipping hand', u'OK hand: medium-dark skin tone',
u'hugging face'],
'curse_rate_s1':2.568,
'curse_rate_s2':1.023,
'laugh_rate_s1':19.323,
'laugh_rate_s2':7.99,
'big_words_rate_s1':None,
'big_words_rate_s2':None,
'longest_streak':17,
'longest_drought':7.013,
'punctuation_s1':None,
'punctuation_s2':None,
'link_rate_s1':1.182,
'link_rate_s2':0.568
}
time_metrics = {
'most_active_day_of_week':'Tuesday',
'least_active_day_of_week':'Thursday',
'most_active_month_of_year':'February',
'least_active_month_of_year':'May',
'most_active_hour_of_day':'14',
'least_active_hour_of_day':'24',
}
curse_rate day_x double_text_rate emoji_rate laugh_rate link_rate participant wait_time
0 2.205882 Monday 7.352941 5.147059 22.058824 1.838235 Me 65.0
1 3.341289 Tuesday 4.057279 5.250597 19.570406 0.954654 Me 20.0
2 1.861702 Wednesday 9.042553 4.521277 19.680851 0.531915 Me 37.0
3 1.219512 Thursday 11.382114 5.284553 14.634146 0.406504 Me 44.0
4 3.546099 Friday 5.673759 5.437352 19.385343 1.891253 Me 22.0
5 1.225490 Saturday 8.333333 6.127451 18.382353 0.735294 Me 38.5
6 4.207120 Sunday 7.119741 3.236246 21.035599 1.941748 Me 38.0
0 0.000000 Monday 15.719064 3.344482 7.023411 1.337793 Friend 32.5
1 0.852878 Tuesday 14.285714 5.330490 7.889126 0.213220 Friend 14.0
2 0.771208 Wednesday 11.825193 3.598972 7.712082 0.514139 Friend 20.0
3 1.127820 Thursday 17.669173 2.631579 9.398496 0.000000 Friend 30.0
4 1.098901 Friday 12.307692 3.516484 7.252747 1.318681 Friend 21.0
5 0.909091 Saturday 14.772727 2.954545 6.136364 0.454545 Friend 28.0
6 2.500000 Sunday 10.625000 2.812500 11.875000 0.000000 Friend 18.0
Its still pretty early but if you have suggestions, thoughts, feedback, criticism, etc feel free to open a PR or submit an Issue.
Thanks in advance 😊
If ya feeling generous, hollr @ the kid ❤️
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