CODEWITHSUNDEEP
machine-learningneural-networknlpword2vecdoc2vec
apgsov
apgsov

Reputation: 904

How to use doc2vec embeddings as an input to a neural network

I'm trying to slowly begin working on a Twitter recommender system as part of a project, which requires me to use some form of deep learning. My goal is to recommend other tweets based on the topical content of a tweet with unlabelled data.

I have pre-processed my data and trained a few variations of models in doc2vec to get both word embeddings and document embeddings. But my issue is that I feel a little lost with where to go from here. I've read that doc2vec can be used as an input to a deeper neural network for training such as an LSTM or even a CNN.

Could anyone help me understand how these document embeddings (and word embeddings, I trained the model on DM mode) are used as input and what the purpose of the neural net would be in this case, is it for clustering? I understand the question is a little open-ended but I'm quite new to all this, any help would be appreciated.

Upvotes: 0

Views: 1291

Answers (1)

Venkatachalam
Venkatachalam

Reputation: 16966

If you have trained a d dimensional doc2vec for each document that will become the input vector for that particular tweet. If you have n number of documents, it will become n*d dimensional matrix. Now, this matrix can be given to the neural network. LSTM and CNN models are all used for supervised learning problems (where you have labeled data).

If you dont have labelled data, then go for unsupervised learning. Clustering comes under this! You can run different clustering algos and recommend based on this.

Upvotes: 1

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