Twitter allows businesses to engage personally with consumers. However, there’s so much data on Twitter that it can be hard for brands to prioritize which tweets or mentions to respond to first. That's why sentiment analysis has become a key instrument in social media marketing strategies. This is a tool that automatically monitors emotions in conversations on social media platforms.It is an automated process of identifying and classifying subjective information in text data. Sentiment analysis uses Natural Language Processing (NLP) to make sense of human language, and machine learning to automatically deliver accurate results.
Basically involves 5 steps:
- Gathering your relevant Twitter data.
- Cleaning your data using pre-processing techniques.
- Creating a sentiment analysis machine learning model.
- Analyzing your Twitter data using your sentiment analysis model.
- Visualizing the results of your Twitter sentiment analysis.
- The Twitter API lets you access and interact with public Twitter data.Use the Twitter Streaming API to connect to Twitter data streams and gather tweets containing keywords, brand mentions, and hashtags, or collect tweets from specific users.
- Tweepyis an easy-to-use Python library for accessing the Twitter API. I used Tweepy for accessing the Twitter API. You need to have a Twitter developer account and sample codes to do this analysis.
- Download the twitter dataset which is avaliable on kaggle. It contains all the tweets based on a paticular entity.