Text Data Sentiment Analysis with Python in TDM Studio

Sentiment Analysis, or Sentiment Classification, is valuable for answering
lots of different research questions, such as: How does public sentiment
react to different tragic events? Do tragic events always elicit the same
negative emotional states? What publicly expressed emotions help
politicians get elected? Have the most common emotions changed over the
last 150 years?
In this 1-hour workshop, we will use TDM Studio to learn about Sentiment
Classification and also run two approaches to the task using Python in
Jupyter Notebooks. One approach will be a baseline dictionary-based
approach and the other a more recent SBERT-based model that performs at the
state of the art for newspaper sentiment classification.
Workshop Agenda
1. Login to TDM Studio
2. Create a dataset related to your topic of interest
3. Run Dictionary-Based Sentiment Analysis
4. Run and compare with SBERT-based model