Sentiment analysis is a field of research born in Natural Language Processing (NLP), aiming at detecting subjectivity in text and/or extracting and classifying opinions and sentiments.
Create a system using Tweepy API and Textblob that will not require a dataset to perform sentiment analysis. With this system we will be able to draw a huge amount of tweets and parse it using python.
In this project, I tried to propose and learn about a system that perform sentiment analysis with a more easier approach that does not require an existing dataset. I have used my Twitter Developer profile with Tweepy API to access the tweets and use Textblob Library for processing textual data.
- Twitter Database
- Tweepy API
- Sentiment Analyzer
Through twitter database we will be able to access the tweets being made.
Python Library which gives a convenient way to access Twitter API with its own set of classes and methods.
Performs sentiment analysis on the filtered tweets.
- Python3
- Google Collab / Visual Studio Code
- Tweepy API
- Twitter Developer Access
- Textblob
- Streaming the tweets
- Accessing the tweets
- Analyzing the tweets
- Visualizing the tweets
- Sentiment analysis
Was successfully able to stream, analyze, visualize the data. After completing the aforementioned steps I was able to perform sentiment analysis successfully.