More Related Content More from Absolutdata Analytics (20) Sentiment analysis quantification of real time brand advocacy for customer journey using sna1. © Absolutdata 2014 Proprietary and Confidential
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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: