I need to create a text classification model to classificate social media posts according to its content (4 labels: eg. promotional, informative, etc.).
I've been studying machine learning in my spare time, so this is gonna help me in my learning journey.
You can use any text classifier (naive bayes, SVM, GBoosting, clustering) as long as it's good enough. It would be a dream if the model was a pre-trained model like BERT or XLNet, but it's not a requirement. Also, it'll be nice if you compare at least 2 classifiers.
The dataset consists of social media posts and it's structured (xlsx) and labelled (by human classification). The data requires minimum cleaning I think.
Please, note that I'll study the code, so you'll need to explain the steps in the notebook provided. I have a fairly good knowledge about machine learning, more conceptual than practice.
P.S.: I have a limited budget! Also, the completion should take no longer than 10 days.
If you have any questions, please ask me.
15 pekerja bebas membida secara purata $180 untuk pekerjaan ini
Hi, I am sure that I can do this job I'm machine learning engineer experienced in natural language processing (NLP) using Python programming. Feel free to contact me for further details. Thanks