Description
Working individuals, top management and investors are in constant turmoil to find the right company from plethora of options. This presentation aims to help find answers for them. An individual could gauge if he’s being underpaid or overpaid. Top management could have a quick, summarized and transparent feedback from their employees. A company could discover about its competitors strengths and weaknesses.
Attendees will learn how to scrape numerical and text data (reviews) from online websites and transform into structured data. Analyze trends and aggregate numerical data at different levels to form the big picture about the company. However, compiling text is a powerful means to understand details and also sentiment of employees. So we form tf-idf matrix, apply dimensional reduction techniques and explore clustering to summarize reviews. Finally, we will delve into simpler methods that are more apt than ML algorithms for this problem statement.