About Practice Problem: Identify the Sentiments
Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. Brands can use this data to measure the success of their products in an objective manner. In this challenge, you are provided with tweet data to predict sentiment on electronic products of netizens.
Data Science Resources
- Get started with NLP and text classification with our latest offering ‘Natural Language Processing (NLP) using Python’ course
- Refer this comprehensive guide that exhaustively covers text classification techniques using different libraries and its implementation in python.
- You can also refer this guide that covers multiple techniques including TF-IDF, Word2Vec etc. to tackle problems related to Sentiment Analysis.
- One person cannot participate with more than one user accounts.
- You are free to use any tool and machine you have rightful access to.
- You can use any programming language or statistical software.
- You are free to use solution checker as many times as you want.
1. Are there any prizes/AV Points for this contest?
This contest is purely for learning and practicing purpose and hence no participant is eligible for prize or AV points.
2. Can I share my approach/code?
Absolutely. You are encouraged to share your approach and code file with the community. There is even a facility at the leaderboard to share the link to your code/solution description.
3. I am facing a technical issue with the platform/have a doubt regarding the problem statement. Where can I get support?
Post your query on discussion forum at the thread for this problem, discussion threads are given at the bottom of this page. You could also join the AV slack channel by clicking on 'Join Slack Live Chat' button and ask your query at channel: practice_problems.
Are you a NLP expert? Different brands monitor their online conversations to measure the success of their product. Here is a challenge for you to predict the sentiments of tweet data of netizens.