This week I focused on finishing the design review presentation and report. I worked on the text emotion recognition portion of the design review and helped design our slide deck. I also incorporated the feedback from the presentations and solidified my architecture for the text sentiment analysis. No matter which model Read more…
I started to work on the text-based emotional analysis. For the first iteration, I’m using Term Frequency-Inverse Document Frequency. This parameter gives the relative importance of a term in the data and is a measure of how frequently and rarely it appears in the text. It’s how we turn words into numbers. Read more…
I’ve finished setting up Python/OpenCV on my local machine and polished our block diagrams. I’ve also acquired the necessary data from the AffectNet and Cohn-Kahnade databases. The AffectNet database provides around 500,000 (~120gb of data) faces categorized into 11 emotions: The Cohn-Kahnade database is significantly smaller and also dated. Thus, Read more…