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."

The Trigger

33

The first impression

Visual

Interset

Personalisation

Need

Trust

Language

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

DataSet

Extracting subject-lines

Removing redundant subject-lines

Sentence alignment

Word Tokenization

preprocessing & corpus preparation

Emotion Analysis

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

Sentiment Analysis

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

Descriptive Analysis

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 .

Word Strength Analysis

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

Observation Analysis

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

Applications

Any Questions ?

Thank you..!