MLH Local Hack Day: Build 2021 Day 2 Challenge: Leverage Sentiment Analysis
Using Sentiment analysis can come in handy for many types of hacks from understanding how customers view a product to determining effective ways of communicating with your audience. Explore this topic and Submit your hack on our Day 2 Devpost!
Simple scripts for scraping sites and performing sentiment analysis in Python using Vader, the Natural Language Toolkit (NLTK) and TextBlob.
Install the code's dependencies:
pip install -r requirements.txt
Input urls you'd like to scrape in the urls.txt file, then run scrape.py:
python scrape.py
The script will save the text it scrapes as .txt files in the project's directory. The script searches for <p> tags by default, but what it looks for can easily be edited in the .py file. Just change the line paragraphs = soup.find_all('p') to search for things other than "p".
Open python, import sentiment.py, and perform sentiment analysis on your texts using Vader, NLTK's Naive Bayes classifier, or TextBlob. The training sentences for the Naive Bayes classifier can easily be swapped out for your own.
>>> python
>>> from sentiment import Sentiment
>>> Sentiment.vader('your_filename.txt')
>>> [sentiment analysis of your text]
for the Naive Bayes classifier:
>>> python
>>> from sentiment import Sentiment
>>> Sentiment.naive('your_filename.txt')
>>> [sentiment analysis of your text]
or for TextBlob:
>>> python
>>> from sentiment import Sentiment
>>> Sentiment.blob('your_filename.txt')
>>> [sentiment analysis of your text]