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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