sentiment analysis quantification of real time brand advocacy for customer journey using sna
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Sentiment Analysis Quantification of Real Time Brand Advocacy for Customer Journey using SNA
Author: Abhishek Sanwaliya (CRM Analytics, Absolutdata)
RapidMiner Community Meeting & ConferencePortugal, Aug 27- 30, 2013
Social Media Trends: Dell Buyout
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Literature Review & FoundationParsed phases and evolution of visualization using SNA
Preliminary Phase
• Aggregated level classification architecture
• Utilized RapidMiner’sraw 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
Social Media Trends: Dell BuyoutExclusion of semantically Insignificant terms
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Text Processing
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
Social Media Themes for ‘Dell Buyout’
Feature Set Preparation:
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Selection of contextually significant keywords
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
Social Media Themes for ‘Dell Buyout’: Key InsightsConclusion: 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
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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Thank You
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