Scanning large amount of textual data to find the most relevant feedback is one of the greatest challenge for marketing. We had enormous amount of unstructured text where a capability was required to quickly identify the main points in a collection of feedbacks. Together with sentiment analysis, key phrase extraction helps in focusing on the top items which needed immediate action. i.e., what are the top 10 key phrases linked to positive sentiments? or What are top 10 key phrases linked to negative sentiment which should be prioritized in order to improve the experience? While it is not realistic to fix all the problem at the same time, we could focus on those 20% of problems improving 80% of the experience, just like the 80/20 pareto principle!

Idea and Solution

Keyword extraction uses machine learning artificial intelligence (AI) with natural language processing (NLP) to break down human language so that it can be understood and analyzed by machines. Using the power of Python and Microsoft Power BI, we implemented a machine learning ML algorithm called RAKE (Rapid Automatic Keyword Extraction) to solve this problem.  It is a keyword extraction method which uses a list of stopwords and phrase delimiters to detect the most relevant words or phrases in a piece of text. So, we leveraged Python + Power BI combination to visualize the key phrases in word clouds and tables.