Topic Modeling vs Sentiment Classification: A Fundamental Difference

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The fundamental difference between Topic modeling and sentiment classification you must know as a data scientist🤖 A thread👇

This thread is derived from my newest article on Cyberbullying detection using Topic modeling and sentiment analysis Check out the article for a more detailed explanation with code👇 analyticsvidhya.com/blog/2023/03/detect-cyberbullying-using-topic-modeling-and-sentiment-analysis/

One of the major differences between topic modeling and sentiment classification is their learning method itself. Topic Modeling is an unsupervised learning technique while Sentiment Classification is a supervised learning technique.

1/ Topic Modeling - There is no need to label large text document - It can identify complex word similarities within one document - It has a lower cost of modeling and inference due to ease of flexibility

2/ Sentiment Classification - One has to label large samples - It is not possible to identify similarities within one single document - It has a higher cost of modeling due to the manual labeling of text samples

There is no need to label large text document has to label large samples can identify complex word similarities within one document is not possible to identify similarities...

...within one single document has a lower cost of modeling and inference due to ease of flexibility has a higher cost of modeling due to manual labeling of text samples.

While Sentiment Analysis is a popular approach used widely in industry, it has many drawbacks which can not be avoided. The cost of labeling each text document would significantly increase which might not be a viable option to have.

Check out my newest article on topic modeling to detect cyberbullying in tweets dataset🤖 Maths and statistics on me, the simplest explanation for you📊 Click the link below to learn more👇 analyticsvidhya.com/blog/2023/03/detect-cyberbullying-using-topic-modeling-and-sentiment-analysis/