Use pretrained word vectors over custom trained word2vecs 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 


use a pretrained word2vec
Train our own word2vec using a dataset related to the domain


Can any one help me on this
Thanks
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 
                                  &amp; 
                    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


Use pre-trained vectors
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.