The solution to most of the advanced data-driven analytics challenges involves easy integration of Python and Tableau. Huge chunks of online data can be used to obtain useful insights, “Sounds great right!!” Yes, it is possible to gain insights using Sentimental Analysis. This blog briefs about advanced analytics functionalities using TabPy from Tableau after exploring the basics of TabPy coding. Before discussing Sentimental analysis using TabPy, let us discuss what is sentimental analysis.
What is Sentimental Analysis?
Sentimental Analysis is the interpretation and classification of emotions expressed in the text, be it the whole document or a clause. It is a text analysis method that detects subjectivity and the polarity of the given text. Polarity in the sentimental analysis is a float number that lies in the range of [-1,1] where 1 means positive statement and -1 means a negative statement. Subjectivity in the sentimental analysis is also a float number that lies in the range of [0,1]. Subjective sentences usually refer to opinion, emotion, or judgment of an individual whereas objective refers to information.
Why is Sentimental Analysis important?
It is a known fact that data is widespread everywhere on the Internet and approximately 80% of the generated data are unstructured. To be more precise huge volumes of text data like emails, support tickets, chats, social media conversations, and so on are getting added every single day. Though there is so much data available it becomes hard to analyze and time-consuming, expensive work without the help of sentimental analysis. But with the existence of sentimental analysis, it becomes easier to organize unstructured text by automatically tagging it with related emotions.
Use Case for Sentimental Analysis
Every organization would wish to cut down customer churn rate to keep up their performance. When looking to cut down customer churn, it is important to analyze customer feedback surveys, customer feedback in social media platforms as well as feedback available in Google Play store or other stores.
All the feedback would be unstructured and that gives us the best use case for sentimental analysis.
TabPy Example for Sentimental Analysis
- Connect to Consumer reviews of Amazon products dataset.
- Drag reviews to rows
- Create a calculated field using Tabpy functions. Here we are using SCRIPT_REAL because we are calculating polarity of the review and the output could either be any positive or negative number ranging between [1,-1]. For the sentimental analysis part, we are using VADER(Valence Aware Dictionary and Sentiment Reasoner). It is a rule-based and lexicon sentimental analysis tool that is specifically available to sentiments expressed in social media.
- Bringing Sentimental analysis calculated field to color mark will help the users to change color according to positive and negative values.
Since we are using Machine Learning capabilities accuracy in polarity scores may differ from one package to another package. Other packages like Spacy, Textblob, and NLTK can also be used to enhance accuracy.
We hope this blog helped you as a guide on the Sentimental analysis using TabPy. To learn more about Visual BI’s Tableau Consulting & End User Training Programs, contact us here.