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A psychological
based analysis of
Marketing Email
Subject Lines
R. Miller,Department of Computer Science,University of Jaffna
Supervisor: Dr. E. Y. A. Charles, University of Jaffna
" Objective of this research is - Finding rules to create
click intensive promotional email subject-lines
via analyzing human psychological intention by text mining
techniques."
Psychological Analysis
Statistical Analysis
•Sentiment Analysis•Subjectivity Analysis•Emotion Analysis
Descriptive Analysis•Word Parser Alignment •Category grouping•tf-IDF score•Word StrengthObservation Analysis•Character count
Corpus.
Facts Rules
Pre-Processing
Motivation & Objective
Extracting subject-lines
Removing redundant subject-lines
Sentence alignment
Word Tokenization
preprocessing & corpus preparation
1. Emotion Analysis
Joy
Sad
Fear
Surprise
Love
Anger
Basic Types of Emotions
Joy
Sad
Fear
Surprise
Trust
Anger
Disgust
Anticipation
Joy
Sad
Fear
Surprise
Trust
Anger
Disgust
Anticipation
PeacePlutchik's wheel of
emotions [1980]
Navarasam emotional flavors
= Lexical + Semantic orientation measure
Lexical-Based Approach
9 Emotion Dictionaries
+ 8500 lexical
+ Semantic orientation measures
Joy
Sad
Fear
Surprise
Trust
Anger
Disgust
Anticipation
Peace
Navarasam emotional flavors
eg: Rent the Runway: "Happy Birthday Lindsay - Surprise Inside !"
37.50%
25.00%
12.50%
25.00%
Peace = Lexical + Semantic orientation measure
S = Semantic orientation Value for Lexical in the dictionary
N = Total # Tokens in the corpus .
n = # Lexical in the dictionary.
SN
n
i=0* 100Proposition of the Emotion type
in the corpus =
0.00%
0.00%
0.00%
0.00%
0.00%
Example of a best conversion Email Subject Line
1010
Rent the Runway: "Happy Birthday Lindsay - Surprise Inside!"37.50 -joy 0.00 - sad 0.00 - fear 0.00 - disgust 25.00 - anticipation 0.00 - anger
25.00 - surprise 12.50 -trust 0.00 -peace
Example of a week conversion Email Subject Line
1111
"I'M DEEPLY HURT THAT YOU DIDN'T REPLY TO MY LAST EMAIL."0.00 - joy 33.33 - sad 33.33 - fear 0.00 - disgust 0.00 - anticipation
33.33 - anger 0.00 - surprise 0.00 -trust 0.00 -peace
Example Heat Map of Emotions in Most Viral Content - http://frac.tl/viral-emotions-study/
Goal: Our goal was to identify the most common
emotions evoked by highly viral image content in
order to gain a basis for understanding which
emotions lend themselves to a viral result most
often.
- fractl
Example: Rent the Runway: "Happy Birthday Lindsay - Surprise Inside!"
Validating Proposed Methodology
2. Sentiment Analysis
"Sentiment Analysis is the process of determining whether a piece
of writing is positive, negative or neutral."-Wikipedia
"Subjectivity is deriving the opinion or attitude of a writer/speaker.
A common use case for this technology is to discover how people
feel about a particular topic. "-Wikipedia
*Sentiment analysis result with 75% accuracy rate: python pattern/en lib.
1 : love the summer but winter not much
3 : kill my loving cat
2 : love the summer but hate the winter
(0.2, 0.4)
(-0.6, 0.95)
Sentiment Value
(-0.15, 0.75)Subjectivity
0.2 , 0.4 -0.15 , 0.75 -0.6 , 0.95
1 32
Sentiment Value
Subjectivity
www.slideproject.com
Correlation between Polarity and Subjectivity
1616
Negative polarity and Subjective
Negative polarity and Objective
Positive polarity and Subjective
Positive polarity and Objective
3. Word Parser Alignment
Tag Description ExampleCC conjunction, and, or, butCD cardinal number five, three, 13%DT determiner the, a, these EX existential there there were six boys FW foreign word mais IN conjunction,
subordinating of, on, before, unless JJ adjective nice, easy... ... ...
* Part-of-speech tags are assigned to a single word according to its role in the sentence.
* Parser alignment assign meaningful tags to words and groups of words in a sentence.
* The overall Pos Tagging accuracy is about 95% (95.8% on WSJ portions 22-24).: python pattern/en lib.
best RB pay NN as IN you PRP go VBP sim JJ card NN
buy VB pay NN as IN you PRP go VBP sim JJ card NN
pay NN as IN you PRP go VBP sim RB uk JJ
pay NN as IN you PRP go VBP sim JJ card NN
W1 pos W2 POS W3 POS W4 POS W5 POS W6 POS W7 POS
This analysis result the best Parser arrangement for a sentence which get more intention to click.
Bi-gram Probability Measure
Bi-gram generation from Google Ad-words Click through Rate(CTR).
Finding the most Frequent grammar alignments which get more clicks and less clicks
Joint probability distribution .
4.1 Word Strength By Bayes Conditional probability
Best words count proportion Bayesian_valuecredit 48 4.8 1.412595644for 36 3.6 1.059446733insurance 33 3.3 0.9711595056as 24 2.4 0.7062978222with 24 2.4 0.7062978222
Neutral words count proportion Bayesian_valuecredit 93 9.3 2.736904061card 39 3.9 1.147733961insurance 30 3 0.8828722778for 22 2.2 0.6474396704cards 20 2 0.5885815185
worst words count proportion Bayesian_valuecredit 82 8.2 2.413184226card 37 3.7 1.088875809insurance 28 2.8 0.824014126for 20 2 0.5885815185and 17 1.7 0.5002942908
1. credit : 1.412595644 - 2.413184226 = −1.000588582
2. insurance : 0.9711595056 - 0.8240141= 0.14714538
4.2 Term Frequency-Inverse Document Frequency
Term Email Collection [tf-IDF] Intensive Email [tf-IDF] Spam Email [tf-IDF]
Thanks 0.000226756214139 0.000149367491492 0.000115150462135
Term Email Collection [tf-IDF] Intensive Email [tf-IDF] Spam Email [tf-IDF]
Journal 8.91737920773e-05 8.23615140001e-06 1.02204552191e-05
An Example for tf-IDF score for an intensive term in Google Adwords
An Example for tf-IDF score for a less intensive term in Google Adwords
5. Sentence word Count & Character Count
#Word Count for a sentence #Character Count for a sentence
Google Ad-Words
4.189252734
23.009111854
8.981899549
46.261758036
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