Quantification of Real Time Brand Advocacy for Customer Journey using Sentiment Analysis. This was Presented in Rapid Miner Community Meeting & Conference, Portugal held on Aug 27-30, 2013 For more details, please visit: www.absolutdata.com

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April 30, 2014
Sentiment Analysis
Quantification of Real Time Brand Advocacy for Customer Journey using SNA
Author: Abhishek Sanwaliya (CRM Analytics, Absolutdata)
RapidMiner Community Meeting & Conference
Portugal, Aug 27- 30, 2013

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Literature Review & Foundation
Parsed phases and evolution of visualization using SNA
Preliminary Phase
 Aggregated level
classification
architecture
 Utilized RapidMiner’s
raw text processing
modules for base level
analysis for exploratory
and extended research
Foundation
 Diagnostic tool to derive
unstructured
information streamed
with social media data
 Enhanced classification
and sentiment analysis
attributed at segment
level
SNA Propagation
 Captures customer’s
perception about
product
 Interactive Visualization
 High magnitude
Sentiment categories
(Scale: -100 to +100)
 Provides reflexive social
media response for
actionable strategy

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Normalization: To obtain a uniform text
Tagging: Part-of-speech (POS) tagging for lexical Marking
Tokenization: Reducing chunk to its colloquial
components
Dimension Reduction: Removal of the non-
context words
Stemming and Lemmatization:
Collapsing derivationally related words
and inflectional forms of a lemma
Text ProcessingText Processing
Exclusion of semantically Insignificant terms

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Clustering of words into groups
of similar concepts
Similarity based on co-
occurrence between two words
in a sentence
Feature set: Original word
document matrix to capture the
semantic coherence of the text
A hierarchical agglomerative
clustering is employed to group
words
Unigram
Feature
Set A co-occurred set that frequently
occurs in a typical sequence
belonging to same class
Follow syntactic sequence of
nearby words having strong
association
Well distinguished co-occurred
phrases having potential to
discern boundary among
available categories
Reduces the complexity in
classification increases with
increase size of feature set
Bi-gram
Feature
Set
Feature Set Preparation:
Selection of contextually significant keywords

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Conclusion: Potential & Scope
Structured categorization and customer sentiment distribution
SNA Tool performance potential:
 SNA: Competent measure to showcase and understand the customer/client’s perception
 A real window implementation to obtain actionable task
 Tuned application of text classification and sentiment analysis to predict the customer perception
 The SNA scoring is capable of showcasing trends at segment and sub segment level
 The proposed SNA scoring and visualization technique signifies the multi-point sentiment scoring
technique
 Synchronized with a real time campaigns to make social media marketing more visible and
actionable
Improvement & scope:
 Limitation of NLP prevents it from reaching a high level of performance (accuracy)
 Difficult to commensurate number of co-located phrases of each class along with sarcastic
statements
 Enhancement of NLP techniques for discriminative view about misclassification
 Capability to compare structured phases of transitions and models for different competitors

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Key Stakeholders & Promoters of SNA (Dell Inc.):
 Rajeev Narang (Exec. Director, Social Media Innovation)
 Munish Gupta (Sr. Consultant, Strategy & New Product Plan)
 Keisha Daruvallla (Marketing Consultant, Social Media Insights & Innovation)
 Anurag Srivastava (Sr. Analyst, Dell Global Analytics)
Engagement:
 Absolutdata (CRM Analytics, India)
 Dell Global Analytics (India)
Organizers:

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