How to use text as an input for a neural network - regression problem? How many likes/claps an article will get
from what I have understood, it's a method used when attempting to predict the next word in a text.

Not really, Word2vec is a technique to represent the (relative) meaning of words in a way that can be fed into a ML model. You can use them as language models, i.e. for the prediction of the next word in a sequence, but that's just one of the possible uses of it. You can train a model with word embeddings for whatever other task. Word2vec is suitable in this case.


Learning word embeddings creates vectors for words that are similar to each other syntax-wise, and I fail to see how that can be used to derive the weight/impact of each word on the target variable in my case.

I don't know your problem well enough, but I'd say you need additional information. For example: informations on the account such as number of followers, likes, shares/retweets, you name it.


In addition, when I tried to generate a word embedding model using the Gensim library, it resulted in more than 50k words, which I think will make it too difficult or even impossible to onehot encode.

Gensim requires a list of words/tokens (in the form of strings), and the word2vec model will take care of anything for you. You don't have to manually one-hot encode all the words. Don't worry about that.


I will have to one hot encode each row and then create a padding for all the rows to be of similar length to feed the NN model, but the length of each row in the new column I created &quot;clean_text&quot; varies significantly, so it will result in very big onehot encoded matrices that are kind of redundant.

I don't really know what you did here, but I'm pretty sure it's not correct. You don't have to manually one-hot encode anything. More importantly, one-hot encoding rows doesn't make sense, why would you do that?