In Today’s era we have a huge un-structured data in many forms via video, image, text etc. Text Analysis is about parsing un-structured text data in order to machine-readable facts from them. The purpose of Text Analysis is to create structured data out of un-structured text content and gather meaningful information from content.
We can easily find text data in Twitter, Facebook, online Grievances portal, online News portals etc. in un-structured form. There are some tools in SAS that can be useful for converting un-structured content into meaningful and appropriate content for analysis.
For our client, we are crawling un-structured text data from online news portal, Twitter handle, Facebook page and from grievance portal for data analytics. The data is further categorized and sentiments are being reviewed. We have created categories as provided by the client such as various departments, on-going issues, further we are assigning the sentiments based on the text received from the dataset. The sentiments are mainly Positive, Negative and Neutral. The process of assigning categories and sentiments is termed as Taxonomy.
The data formed is then used for visualization in which we showcase major issues, trends location wise to resolve society issues pro-actively.
SAS Content Categorization, SAS Sentiment Studio and SAS Visual text Analytics or we can call Taxonomy (will be covered in the next blog) are those tools meant for text analysis. By using SAS Content Categorization and SAS Sentiment Studio we can make data meaningful for BI report and analysis.