Sentiment analysis quantification of real time brand advocacy for customer journey using sna

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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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Sentiment analysis quantification of real time brand advocacy for customer journey using sna

  1. 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com 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
  2. 2. © Absolutdata 2014 Proprietary and Confidential 2 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
  3. 3. © Absolutdata 2014 Proprietary and Confidential 3 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
  4. 4. © Absolutdata 2014 Proprietary and Confidential 4 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
  5. 5. © Absolutdata 2014 Proprietary and Confidential 5 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
  6. 6. © Absolutdata 2014 Proprietary and Confidential 6 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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