I decided to have some more fun
with the April
2014 General Conference talks. This time, I applied some basic text
analytics and natural language processing techniques to estimate each talk's
overall sentiment. In general, a talk with "positive sentiment" uses
more optimistic type words and a talk with "negative sentiment" uses
more pessimistic type words.
Below is a chart summarizing the sentiment
differences for each talk, but only based on adjectives and adverbs a computer algorithm
automatically identified for me. Adjectives and adverbs tend to be the types of
words that convey sentiment. Surprisingly, Talk #17 -- Love—the Essence of the
Gospel -- had the most negative sentiment! Go figure! Talk #16 -- Live True to
the Faith -- had the most positive sentiment! (Cell phone users - click/tap image to expand and see fine text)
[1] A Priceless Heritage of Hope
[2] Are You Sleeping through the
Restoration
[3] Be Strong and of a Good Courage
[4] Bear Up Their Burdens with Ease
[5] Christ the Redeemer
[6] Daughters in the Covenant
[7] Fear Not; I Am with Thee
[8] Following Up
[9] Grateful in Any Circumstances
[10] I Have Given You an Example
[11] If Ye Lack Wisdom
[12] If Ye Love Me, Keep My
Commandments
[13] Keeping Covenants Protects Us
Prepares Us and Empowers Us
[14] Let's Not Take the Wrong Way
[15] Let Your Faith Show
[16] Live True to the Faith
[17] Love—the Essence of the
Gospel
[18] Obedience through Our
Faithfulness
|
[19] Protection from Pornography—a
Christ-Focused Home
[20] Roots and Branches
[21] Sisterhood Oh How We Need Each
Other
[22] Spiritual Whirlwinds
[23] The Choice Generation
[24] The Cost—and Blessings—of
Discipleship
[25] The Joyful Burden of
Discipleship
[26] The Keys and Authority of the
Priesthood
[27] The Priesthood Man
[28] The Prophet Joseph Smith
[29] The Resurrection of Jesus
Christ
[30] The Witness
[31] Wanted Hands and Hearts to Hasten
the Work
[32] What Are You Thinking
[33] What Manner of Men
[34] Where Your Treasure Is
[35] Your Four Minute
|
I also made a second chart showing sentiment differences, but this time it was based on all words which the sentiment lexicon/dictionary
could identify and was not limited to just adjectives and adverbs. With this
method, Talk #5 -- Christ the Redeemer -- ended up having the most negative
sentiment! Talk #13 -- Keeping Covenants Protects Us Prepares Us and Empowers
Us -- had the most positive sentiment.
Overall, this was a fun exercise to
see how general conference talks vary sentiment-wise. The methodologies and
computer algorithms utilized for this analysis are by no means perfect, but
they are surprisingly useful to get additional insight and extract information
from text.
***
Summary of methodology:
·
R
was used for all analysis/visualization. Libraries tm, NLP, openNLP, proxy,
RWeka, openNLPmodels.en, and stringr used.
·
Each
conference talk was copy and pasted into its own text file to make a corpus of
documents.
·
Harvard's
Inquirer Lexicon/Dictionary was used to estimate sentiment, but with some modifications
by myself. This dictionary can be accessed at these links: http://www.wjh.harvard.edu/~inquirer/homecat.htm
(general page) or directly at http://www.wjh.harvard.edu/~inquirer/spreadsheet_guide.htm
·
Sentiment
was estimated for each talk individually by taking the total number of positive
sentiment words and dividing it by the total number of all sentiment words
(positive + negative) the Lexicon could identify.
- The mean sentiment
for the group of 35 talks was 72%. I.e. on average, of all the sentiment type
words identified, 72% of them were positive in the talks.
