Skip to content

Latest commit

 

History

History
30 lines (21 loc) · 1.42 KB

README.md

File metadata and controls

30 lines (21 loc) · 1.42 KB

Balenciaga PR Scandal Analysis: Sentiment and Brand Perception

Overview

This repository analyzes over 2 million tweets from the Balenciaga PR scandal to explore public sentiment shifts and inform crisis management strategies.

Key Features

  • Data Collection: Tweets collected using the Twitter API and preprocessed by cleaning, translation, and filtering.
  • Sentiment Analysis: Leveraged VADER and Text2Emotion to classify emotions like anger, sadness, and happiness.
  • Quantitative Analysis: Applied t-tests and Regression Discontinuity Design (RDD) to evaluate PR impact and explore cultural differences.

Repository Structure

  • scarping_tweets.ipynb: Code for collecting tweets using the Twitter API.
  • translation.py: Script for translating non-English tweets.
  • merging_translated_parts.py: Script for merging translated tweet files into a single dataset.
  • process_sentiment.py: Script for cleaning tweets, analyzing sentiment, and extracting emotions.
  • RDD_data_analysis.ipynb: Notebook for analyzing sentiment shifts and performing Regression Discontinuity Design (RDD).

Key Findings

  • Apologies tend to increase negative emotions like sadness and anger temporarily.
  • Discount campaigns improve positive sentiment and surprise in the long term.

Contact

For questions and data, please reach out via [email protected].