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Currently i'm working on a sentiment analysis research project using LSTM networks.

As the input I convert sentences into set of vectors using word2vec.

And there are some well pretrained word vectors like Google word2vec.

My problem is, is there are any advantages of using custom trained word2vecs(train using a dataset which related to our domain, such as user reviews of electronic items) over pretrained ones.

Whats the best option

  1. use a pretrained word2vec

  2. Train our own word2vec using a dataset related to the domain

Can any one help me on this Thanks

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  • $\begingroup$ An attempt at a similar question: datascience.stackexchange.com/questions/26419/… $\endgroup$ – redhqs Feb 24 '18 at 9:32
  • $\begingroup$ Thanks Gyan Rangjan for your answer, I'm also interested in that topic. What is for you enough data to train "decent" in-domain word vectors? $\endgroup$ – Pok Sep 4 '18 at 13:05
  • $\begingroup$ I don't think there is a 'defined' number for what data is enough . Interestingly , even with average sized data , some semantic relations do get captured . However , and it's true for any deep learning model , the more data you have , the better result you get . It's easier to tell , what data size is not enough though . Don't expect embeddings to be good if you just have a few hundred sentences. $\endgroup$ – Gyan Ranjan Sep 5 '18 at 12:22
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The entire philosophy of Distributed Word Representations makes use of the fact that a word is understood by the context it has . When we say context , we mean the words that come in the neighborhood of a particular word . Now context in natural language is a tricky thing . As an example . the words open and create are two words that are not so similar semantically . But think of the scenario of a Bank related corpus , you will frequently see statements like

                     How do I open a new account 
                                  & 
                    How do I create a new account

In the context of such a document , open and create become similar words. That is why context specific word vectors are needed.

Now , when it comes to which is the better option between the two

  1. Use pre-trained vectors
  2. Use custom vectors ,

it depends on how much data do you have for your custom use case . If you have enough data then it's always safe/better to go for a custom vectorization as it will be very specific to the context the corpus has . But in all other cases , you can use the pre trained embeddings. These generalize quite well on a variety of docs.

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