This work has been showcased to the PythonBiellaGroup community on 20/12/2021.
https://pythonbiellagroup.it/it/
Sentiment Analysis, or Opinion Mining, is a sub-field of Natural Language Processing (NLP) that tries to identify and extract opinions within a given text The aim of sentiment analysis is to gauge sentiments, evaluations, attitudes and emotions of a speaker/writer based on the computational treatment of subjectivity in a text.
Why perform sentiment analysis?
- Manage critical posts on social media
- Improve the Customer Experience
- Assess the impact of sponsorships
- Discover new market trends
- Maintain the quality of the service on a national,international and global scale
The purpose of the project is to perform sentiment analysis about a product (Apple AirTag) gathering tweets by scraping. After that all results are showcased on a static report built with Datapane and a data app built with Streamlit.
Here's the pipeline
- Scraping: gather tweets with Twitter API and the library tweepy
- Text preprocessing: text cleaning and transformations with NLTK and tweet-preprocessor
- Sentiment Analysis: performed with TextBlob. There is a comparison with Azure Cognitive Service and VADER in the notebook also.
- Data Visualization: made with Plotly
- Data Consumption: static report made with Datapane and Data App with Streamlit
| Name | Link |
|---|---|
| Tweepy | https://www.tweepy.org/ |
| NLTK | https://www.nltk.org/ |
| tweet-preprocessor | https://pypi.org/project/tweet-preprocessor/ |
| TextBlob | https://textblob.readthedocs.io/en/dev/ |
| Ploltly | https://plotly.com/ |
| Datapane | https://datapane.com/ |
| Streamlit | https://streamlit.io/ |
https://datapane.com/u/airaghidavide/reports/O7vxBpA/apple-airtag-sentiment-analysis/
