Introduction to Sentiment Analysis using Python (intermediate level)

The Hot Source! project, funded by AHRC, is organizing an online workshop on March 13th, 2024, as part of its training activities from 1:00 pm – 3:00 pm. 

This workshop is designed for individuals who are interested in learning about sentiment analysis and its applications. Sentiment analysis is the study of extracting and understanding emotions from textual data, along with its diverse applications. 

Participants will be introduced to the concept of sentiment analysis and its relevance in various fields. They will also learn about different techniques for pre-processing text data for sentiment analysis. 

The workshop will cover both rule-based approaches and machine-learning algorithms for sentiment analysis. Attendees will learn how to implement rule-based approaches to analyse sentiment in text data. They will also be introduced to popular machine learning algorithms such as Naive Bayes (or Support Vector Machines (SVM) for sentiment analysis. 

Throughout the workshop, participants will have the opportunity to evaluate sentiment analysis models and understand the importance of model evaluation in achieving accurate results. They will learn about different evaluation metrics and techniques for assessing the performance of sentiment analysis models. 

This course is suitable for individuals with a good knowledge of Python as a programming language and an interest in natural language processing and text analysis. By the end of the workshop, participants will have a solid understanding of sentiment analysis techniques and be able to apply them in various domains. Additional resources from intermediate-level workshops are available on topics such as Introduction to NER, Introduction to Pandas, and Topic Modeling. 

In addition to this workshop, participants can register for a 1:1 ‘Practical Surgery’ session on 27^th  March. 

Registration: https://www.eventbrite.co.uk/e/workshop-introduction-to-sentiment-analysis-using-python-tickets-773159499877