Hi Jonathan,
Word embeddings project discrete words into high-dimensional dense vectors with the aim of preserving word meaning into the embedding space. More details here: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
The TweetToEmbeddingFeatureVector creates a sentence-level representation by aggregating the embedding values of the words within a sentence. Aggregation can be done by averaging, adding, or concatenation. The default configuration of the filter uses pre-trained word vectors of 100 dimensions  and averages the word vectors withing a sentence. This is because you are getting 100 attributes (e.g., embedding-0, embedding-1, etc).
You can also train your own word embeddings using the filters provided by the WekaDeepLearning4j package.
I hope this helps.
Cheers,
Felipe

On Thu, Aug 23, 2018 at 3:16 PM Jonnathan Carvalho <joncarv@gmail.com> wrote:
Hi Felipe,

As you have suggested, I used the AffectiveTweets package to get the word embeddings for tweets in Weka, using its default parameters, but I couldn’t understand what the generated dimensions mean (from embedding-0 to embedding-99)...

Do you recommend any reading?

Thanks a lot!

Cheers,
Jonnathan.

On 22 Aug 2018, at 01:16, Felipe Bravo <felipebravom@gmail.com> wrote:

Hi,
Yes you can get a document-level representation from pre-trained embeddings using the AffectiveTweets package (https://github.com/felipebravom/AffectiveTweets) or can even train your own embeddings using the deeplearning package (https://deeplearning.cms.waikato.ac.nz/). 
Cheers,
Felipe

On Wed, Aug 22, 2018 at 11:03 AM Jonnathan Carvalho <joncarv@gmail.com> wrote:
Hi, All!

I’m trying to figure out what word embeddings is...

Is it possible to use the dense feature representation generated by this technique with learning algorithms such as SVM? Or with neural networks only?

Does Weka support word embeddings?

Thanks!
Cheers!
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Cheers,
Felipe
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Send posts to: Wekalist@list.waikato.ac.nz
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Cheers,
Felipe