Hi. Very new to this forum and more to Tensorflow, and I had a quick question that I hope someone can help me in.
Basically, I’m working with a set of languages (hausa, yoruba and igbo) that do not have a reliable sentiment analysis model to process text with - unless I missed something. What I want is to create a custom model for each of these languages where the model scores and returns the sentiment of a sentence as accurately as possible.
I’m not sure how to approach this. What I did first is got a training dataset where the text and its sentiment (human scored), vectorized the text and created a model (using an Embedding layer). The accuracy wasn’t the best but I don’t know if that is the way to continue. Selecting the right hyperparameters seems like a separate job on its own.
Can anyone recommend on how you might approach this? And if there’s documentation on how a sentiment analysis model using these languages (or any non-English) language is created?
Any help would be appreciated. Thanks.