SpringerNLP/Chapter6

Chapter 6: Convolutional Neural Networks

28
/ 100
Experimental

This project helps you analyze customer feedback, like social media comments or survey responses, to understand their sentiment towards a product or service. You input raw text data, such as tweets about airlines, and it provides an assessment of whether the sentiment expressed is positive, negative, or neutral. This is useful for market researchers, brand managers, or customer experience analysts.

No commits in the last 6 months.

Use this if you need to quickly gauge public opinion or customer feelings from large volumes of text data, specifically for sentiment analysis.

Not ideal if you require advanced natural language understanding beyond sentiment, or if you don't have experience running Docker containers.

sentiment-analysis customer-feedback brand-reputation text-analytics market-research
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 15 / 25

How are scores calculated?

Stars

9

Forks

5

Language

Jupyter Notebook

License

Last pushed

Jul 23, 2019

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/SpringerNLP/Chapter6"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.