Problem

Our client, a marketing agency, wanted to understand the sentiment of their brand on Twitter. They were struggling to manually analyze thousands of tweets and needed an automated solution to quickly and accurately classify tweets as positive, negative or neutral.

We knew that building a machine learning model would be the best approach but we had to overcome several challenges. Firstly, we had to find a way to collect enough data for our model to be accurate. Secondly, we had to ensure that our model was able to handle the nuances of human language and accurately classify tweets.

Click here for a video explanation of our approach.

Solution

We decided that building a machine learning model using natural language processing (NLP) techniques would be the most effective solution. We used Python's Scikit-learn library and NLTK package for data preprocessing and feature extraction.

To collect enough data for our model, we used Twitter's API to scrape thousands of tweets related to our client's brand. We then preprocessed the data by removing stop words, stemming words and converting all text into lowercase. We also used TF-IDF vectorization technique for feature extraction.

Impact

Improved accuracy in classifying tweets by X%
Reduced time spent on manual analysis by X%

"This project has revolutionized how we analyze social media sentiment at our agency."

Team

Team Member #1 - Data Scientist
- Built and trained the machine learning model using Scikit-learn and NLTK packages
- Performed data preprocessing and feature extraction on the collected tweets
Team Member #2 - Software Engineer
- Built the web application for our client to easily input their brand name and view sentiment analysis results in real-time
- Integrated Twitter's API for data collection into the web application

Automating Twitter Sentiment Analysis for Brands