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Supplier & Product Rating based on Customer Feedback : HANA Sentiment analysis

Introduction


With this quick blog I am explaining how to implement a supplier rating application using the Text Analysis capabilities of SAP HANA. I am using Sentiment analysis feature to rate a particular product and suppliers based on the customer feedback on various web sites .

 

Assumptions

We have the customer feedback collected from suppliers and arranged in the following format. (data is copied from multiple online suppliers. Product names and supplier names are masked )

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data strucure.JPG

 

Modelling your source Table

     Now we need to load the data into SAP HANA. For that, We are creating a table of the same structure .

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Query.JPG

 

After once the data is loaded into the source table, we are creating a full text index on the table for the FeedbackText column using the standard configuration "EXTRACTION_CORE_VOICEOFCUSTOMER".

Voice of the customer content includes a set of entity types and rules that address requirements for extracting customer sentiments and requests. You can use this content to retrieve specific information about your customers' needs and perceptions when processing and analyzing text. The configuration involves complex linguistic analysis and pattern matching that includes processing parts of speech, syntactic patterns, negation, and so on, to identify the patterns to be extracted.

Once the index is created , one new columnar table will be created in the same schema where the source table is created. This table will be having the tokens as well as the corresponding sentiments values .

 

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Tree.JPG

 

Now generating some visualizations on the generated sentiment data.

 

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Analysis.JPG

 

SAP Lumira For Reporting


Now we have the sentiment data available in our table. That means, we have the product details, Supplier details, and the type of feedback given by the customers . Now we will generate dashboard(Real time dashboards on LUMIRA if we integrate it with live feedback data on multiple web sites) .

 

 

 

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Pie Chart.JPG

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I love this part.JPG

(This blog is created for DataGeek ).

 

Sreehari


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