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Camera ready sentiment analysis : quantification of real time brand advocacy for customer journey using sna
 

Camera ready 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. ...

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

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    Camera ready sentiment analysis : quantification of real time brand advocacy for customer journey using sna Camera ready sentiment analysis : quantification of real time brand advocacy for customer journey using sna Document Transcript

    • Sentiment Analysis: Quantification of Real Time Brand Advocacy for Customer Journey using SNA Synopsis of Proposed Paper MRSI 21st Annual Market Research Seminar Abhishek Sanwaliya Senior Analyst, CRM AbsolutData Inc., India Anurag Srivastava Senior Analyst, DGA Dell Inc., India Munish Gupta Marketing Director Dell Inc., Texas Keisha Daruvalla Marketing Consultant Dell Inc., Texas
    • Abstract Social media marketing propagates the need of trend analysis to capture brand advocacy. The presence in social sphere can be judged by the segmented sentiments which quantify product’s brand image. The sentiment analysis is benchmarked as a key indicator to measure brand advocacy. One way to tap into the potential of unstructured data is through text analytics. Text analytics is the practice of semi-automatically aggregating and exploring textual data to obtain new insights by combining technology, industry knowledge, and practices that drive business outcomes. The text processing conceptualizes effective text filtration and classification. The stabilized Social Net Advocacy (SNA) platform projects structured visualization of sentiment analysis to measure sentiments for structured customer journey. Introduction The virtue of social media presence has brought the need of real time sentiment monitoring to evaluate the brand advocacy. The best practice utilizes key text mining modules, such as categorization and sentiment analysis. The past work done is this domain was significant when measured for real time monitoring augmented by effective visualization. The previous practice was constrained to provide analysis at broad level with no segment level attribute. Social Net Advocacy (SNA# ) tool is developed to analyze segment level customer journey (sentiment score) with effective visualization. The pre-filtration task performed by RapidMiner [2] provides the opportunity to explore and expand the research for structured sentiment analysis [8]. Data from several social media sources are fed into the tool, which provides insights into customer sentiment on different product lines, each feature layered to sub-products. It is a diagnostic tool developed to take insights from unstructured information available through active social media providers such as Facebook, twitter, blogs, forums etc. The final advocacy measure is one of the key assessments to measure brand advocacy for social presence of the product. # Social Net Advocacy (SNA) Tool is at Beta version stage which is researched and maintained by Social Media Team at Dell Inc.
    • SNA provides ease of visualization that helps to figure out customer’s share of voice about products, components, stages of the customer journey (for both commercial and consumer customers), and business functions. Each category and subcategory includes a breakdown of sentiment—positive, negative and neutral, along with the number of posts devoted to that topic and the change in SNA score over the past time frame. This real time feature provides reflexive social media response that can set the guidelines for actionable strategy to channelize social media marketing. Methodology The contributed research captured the essential of text mining modules to get structured sentiment analysis using SNA visualization feature. The RapidMiner’s capability of handling unstructured data using text processing modules (viz. tokenizing, stemming, filtration, term frequencies, document frequencies & TFIDF) induced proactive text-content management and utilized to evaluate the raw text processing filtration efficiency. The exploratory advanced implementation of structured classification and sentiment scoring scaled on broad spectrum (- 100 to +100) using SNA tool. The SNA score gives quantified measure and sense of brand advocacy for structured customer journey. The typical sequence synchronized and developed the segmented score valuation system with measurable flow of social media data aggregation, processing, tuning, classification and sentiment scoring. Text Preprocessing In order to produce efficient results with high accuracy, first we excluded the terms that were semantically insignificant. We apply basic modules for text pre- processing we ensure the data sanity for better results. Table 1 includes pre- processing steps: Table 1 Table 1: Pre-processing of Text Module Description Normalization To obtain a uniform text we adopt normalization in which we Convert text to lowercase so that the distinction between uppercase and lowercase is ignored.
    • Module Description Tagging Part-of-speech (POS) tagging is the process of assigning a part-of- speech such as noun, verb, pronoun, preposition, adjective or other lexical class marker to each word in a sentence. It is based on ―The Penn Treebank Tag set‖ [7] Tokenization Tokenization is the process of reducing a message to its colloquial components Dimension Reduction Dimensionality reduction is a process to reduce the space of a document. Removal of the non-context words that occur with very high frequency in most documents and do not carry any semantic meaning for categorization and hence are insignificant in making distinction among different documents. Stemming and Lemmatization Stemming most commonly collapses derivationally related words, whereas lemmatization only collapses the different inflectional forms of a lemma.[5] Combined effect of text filtration yields a feature set having huge potential to produce efficient and effective classification. Dimension reduction provides improved efficiency by selecting relevant terms to prepare a feature set. As a result of stemming and lemmatization effective feature set is prepared to achieve higher efficiency. Pre filtration has major impact on classification accuracy. Feature Set Preparation A feature set is defined as a set of words (or phrases) that specifies a particular class and accordingly helps a classification algorithm to discern the boundary of a class from that of another. We used unigrams and bigrams, extracted from text, as contents of feature set. However we use this methodology because we want to focus on identifying a distinct set of features. We made use of two types of feature sets: (1) Bi-grams approach: a set consisting frequently
    • occurring word with a particular class and (2) Unigram Approach: a word cluster with similar semantic context. They are based on the concept of word co-occurrence. A. Bi-gram Feature Selection A co-occurred phrase is a word pair that frequently occurs in a typical sequence in documents belonging to a same class. It is not necessary that they follow same sequence but should follow syntactic sequence of nearby words. It enables us to prepare well distinguished co-occurred phrases which are strongly associated with its class and have a potential to discriminating among available categories. Another facet is to select terms having high frequency in order to reduce the noise of data. Complexity in classification increases with increase size of feature set. For efficient and prominent classification, however, is not easy to select such a set of word pairs due to the inherent complexities, a strong association between a bigram and a class is necessary. A number of bigrams is initially compiled after removing stop-words. To determine a strong association between a bigram and a particular class, the information gain measure was employed [3].To determine the characteristic of a category we concentrate on sentences containing topic tag names explicitly and then consolidate whole information to determine the class. Correlation between bigrams and class clearly determines the category of customer journey. B. Unigram Feature Selection Another method for identifying a feature set is unigrams approach that utilizes clustering of words into groups of similar concepts. The word similarity is estimated by co-occurrence between two words in a sentence. Word similarity index provides good approximation of co- occurrence. In this approach, we measure the similarity by computing cosine angle between two word vectors. This provides an insight that more the number of co-occurred word in a document, higher would be the similarity value. We applied the concept of Latent Semantic Analysis (LSA) due to lack of semantically similar group detection ability of conventional weight based methods. To capture semantic coherence we use LSA [6]. We represent our feature set as an original word document matrix to capture the semantic coherence of the text. Then we extract most important single factor to measure the covariance of inverted matrix. This way LSA captures the ―semantic‖ value of given text document. It becomes easier to trace the similarity of documents using LS as it represents text in subjective way as compared to conventional approach in which
    • all weight goes to high frequency terms. A word in the identified vocabulary is represented as a word vector. A hierarchical agglomerative clustering [4], [1] is employed to group words. Unigram enables us to prepare feature set that can provide better results. Classification: Decision Tree is a basic flow technique that selects labels for input values. This flowchart consists of decision nodes, which check feature values, and leaf nodes, which assign labels. To choose the label for an input value, we begin at the flowchart's initial decision node, known as its root node. This node contains a condition that checks one of the input value's features, and selects a branch based on that feature's value. Following the branch that describes our input value, we arrive at a new decision node, with a new condition on the input value's features. We continue following the branch selected by each node's condition, until we arrive at a leaf node that provides a label for the input value. Once we have a decision tree, it is straightforward to use it to assign labels to new input values. What's less straightforward is how we can build a decision tree that models a given training set. But before we look at the learning algorithm for building decision trees, we'll consider a simpler task: picking the best -decision stump- for a corpus. A decision stump is a decision tree with a single node that decides how to classify inputs based on a single feature. It contains one leaf for each possible feature value, specifying the class label that should be assigned to inputs whose features have that value. In order to build a decision stump, we must first decide which feature should be used. The simplest method is to just build a decision stump for each possible feature, and see which one achieves the highest accuracy on the training data, although there are other alternatives that we will discuss below. Once we have picked a feature, we can build the decision stump by assigning a label to each leaf based on the most frequent label for the selected examples in the training set (i.e., the examples where the selected feature has that value). Given the algorithm for choosing decision stumps, the algorithm for growing larger decision trees is straightforward. We begin by selecting the overall best decision stump for the classification task. We then check the accuracy of each of the leaves on the training set. Leaves
    • that do not achieve sufficient accuracy are then replaced by new decision stumps, trained on the subset of the training corpus that is selected by the path to the leaf. Figure 1: Supervised Machine Learning Approach Experimental Results: SNA Analysis This section discusses the sentiment analysis experimented over real time social media posts for one month (April’13) (arbitrated for sake of confidential interest). The pre-processing and data preparation created efficient and clearly defined boundary for respective categories. The aggregated data for product line classified under two broad categories viz. customer journey - consumer and commercial. The classified categories synchronized with SNA tool to visualize the sentiment trends both at macro and micro level. Figure 2: Post Volume & SNA Trend for Customer Journey-Consumer
    • The SNA tool provides layered sentiments distributed across themes captured for customer journey. Figure 3: Post Volume & SNA Trend for Consumer Segment The fluctuation in SNA trend (Fig. 3) along with post volume distribution signifies the aggregated raves, rants and neutral sentiment across consumer segment. The advocacy can be visualised by SNA scores associated with key themes depicted for customer journey across prime consumer segments. Figure 4: Post Volume & SNA Trend for Customer Journey-Commercial
    • The commercial segment contains less post volume (Fig. 4) due to Business-to- Business nature, which yields less social media presence as compared to consumer segment. Figure 5: Post Volume & SNA Trend for Commercial Segment The product related posts lead the segment but with neutral sentiment. Components and quality and reliability related issues ranted with negative SNA but performance, order management, fulfillment, and services show positive footprints. The SNA is competent measure to showcase and understand the customer/client’s perception and journey throughout till the deal get accomplished. This shows a real window that can be implemented as an actionable task and synchronized with a real time campaigns to make social media marketing more visible and actionable. Conclusion By taking insights from available social media posts, we introduced application of text classification and sentiment analysis to predict the brand advocacy by accessing the customer journey - consumer and commercial. Text classification enormously utilizes the combined concepts of feature extraction and information gain along with sentiment scoring using SNA. The SNA scoring is capable of showcasing trends at segment and sub segment level. With the proposed SNA scoring and visualization technique, we are able to signify the brand advocacy in terms through the multi-point sentiment scoring technique which has the capability to score over
    • 100 point scale with pin point magnitude measurement. The successful results support that the proposed algorithms effectively work in this task, though the domain of classification problem was confined; despite this we obtained promising result. However, the proposed method has several weak points associated with NLP engine limitation that prevent it from reaching a performance above 70 % accuracy when measured on precision accuracy for supervised learning. Failure of our approach takes place when we have commensurate number of co-located phrases of each class and sarcastic statements, as it is then difficult to determine the class. To cope with these problems, we consider employing several natural language processing techniques that can provide discriminative view about misclassification. We are also projecting the approach using discriminate term extraction with coherent semantic structure. We are developing the capability of SNA through which we can do competitor analysis keeping taxonomy structure and phases of transition. Acknowledgements We would like to take this immense opportunity to express our sincere gratitude toward our mentor Mr. Rajiv Narang (Executive Director, Dell Inc.),who incubated the concept of SNA. It would have never been possible for us to take this research to destination without his ideas, relentless support and encouragement. Word of appreciation to Absolutdata for their text mining engagement with Dell Global Analytics (DGA). Special thanks to Mr. Sudeep Goswami (Senior Manager, Strategy Marketing and Sales Analytics, DGA) for showing confidence in us and providing us with a great incubation environment to shape this research at DGA. Thanks to Mr. Guhan P (Sr. Business Advisor, DGA) for his expert mentorship.
    • References A. Jain and R. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988. Rapidminer http://rapid-i.com A. McCallum and K. Nigam. Employing EM and pool-based active learning for text classification. In Proceedings of International Conference on Machine Learning, pages 359–367, 1998. S. Dumais. Using svms for text categorization. IEEE Intelligent Systems, 13(4), 1998. Porter Stemmer, http://tartarus.org/~martin/PorterStemmer S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391– 407, 1990. Penn Treebank Tag set http://www.ims.unistuttgart.de/projekte/Corpus Workbench/CQP-HTMLDemo/PennTreebankTS.html Minqing Hu. and Bing Liu. Mining and summarizing customer reviews, KDD '4: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, 2004 Thanks San Francisco | Chicago | New York | London | Dubai | New Delhi | Bangalore | Singapore