Building a text classification model from scratch

Question

I am a beginner in data science and machine learning techniques. I would need to build a model that allows me to classify texts based on sentiment analysis. Right now I only have the text and they miss any class nor any information about sentiment analysis.

Data collected (texts) are approximately 50000 and they are already cleaned of punctuation and stop words. I heard about the possibility to build some neural networks or use a logistic regression, but I do not know anything about specific models to use for that.

Furthermore, I might consider to build a new model from scratch (I know that can take ages and a lot of efforts) but I'd like to know what I need (for example, already existing model/dataset to train with dictionaries and sentiment analysis). The problem is that what I would like to do is to classify a text as positive or negative (sentiment); also, classify it as fake or not fake.

Do you have any suggestion or advice? If you need further information, I would be happy to provide them.

Answer 1

To train a model on your collected data, you need to label your text first. For example, given a sentence

Cats can fly.

It need to be labelled as 'Faked'. Otherwise your model wouldn't know how to classifier between 'Faked' and 'Not Faked'.

However, labelling needs huge effort. So if your data doesn't have any label, I suggest you use a trained model to predict or train a model on an existing sentiment classification dataset. You can search on the Google for the appropriate model of sentiment analysis.