Sentiment Analysis: Customer Survey Questions | Ankita Guha

Sentiment Analysis using Natural Language Processing Algorithms are often used to understand overall Customer Satisfaction. Survey questions and their respective answers constitutes an important subset in the context of textual analysis. Often a holistic overview of Customers’ needs, or request could be upheld by understanding what most of the Customers are looking for. After initial data cleaning all answers are converted to corpus of words. These word corpuses are next converted to lower case letters. Stop-words (such as the conjunctions, interjections), stemming words (such as adjective, adverbs), numbers, contextual specific words etc. are all removed. Exploratory data analysis such as Words Clustering could shed some light on the frequency of the number of words that appeared in the answers. Hierarchical Clustering Algorithms can be used to understand the words or group of words that are used together based on their frequency distance in a sentence. Based on the frequency of the words each Cluster forms a tree of definite height. Combination of words used in a sentence and to interpret correlation amongst those words, these Cluster trees could be cut at a definite height either from Upper or from Lower Branch. Higher the correlation limit, higher are the associativity of these various words with one another. Using Bi-Gram and Tri-Gram most commonly using phrases could be identified. Various statistical libraries are used to compute the overall sentiments of the customers as well.
This workshop was conducted by Ankita Guha, Freudenberg Group
Learn more about WiDS Workshops: widsconference.org/workshops
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[@jianninap]

Where could we find the toolkit Ankita was going to share after the workshop? Thank you!

[@widsworldwide]

checking on this

[@indus2059]

@@widsworldwide Hello: Just following up on the earlier question. Any updates would be appreciated. Thanks in advance.