vishnux / sentiment-analysis-using-bert

Twitter Consumer Sentiment Analysis

Sentiment-Analysis-using-BERT

This project is aimed at analyzing the consumer sentiment towards arts and cultural experiences in British museums by using the BERT (Bidirectional Encoder Representations from Transformers) model. The sentiment analysis is performed on a dataset of 2+ years of tweets related to museum experiences.

Dataset

Wang, Bo; Tsakalidis, Adam; Liakata, Maria; Zubiaga, Arkaitz; Procter, Rob; Jensen, Eric (2016): SMILE Twitter Emotion dataset

Methodology

The BERT model is used to perform sentiment analysis on the dataset. The model is trained on the dataset and then used to classify the sentiment of each tweet in the dataset as anger, disgust, happiness, surprise or sadness. The results of the sentiment analysis are then used to determine the overall consumer sentiment towards arts and cultural experiences in British museums.

Installation

To run this project, you need to install the following dependencies:

• Python 3.6+
• PyTorch
• Transformers

Results

The sentiment analysis results show the overall consumer sentiment towards arts and cultural experiences in British museums. The results can be used to identify areas where improvements can be made to enhance the visitor experience and increase visitor satisfaction.

Conclusion

Sentiment analysis using the BERT model can be a useful tool for analyzing consumer sentiment towards arts and cultural experiences in British museums. The results can be used to identify areas for improvement and enhance the overall visitor experience.