The deep learning models like BidirectionalLSTM or LSTM from both of them which is better in term of perfomance for text and sentiment classification?
https://arxiv.org/pdf/1801.07883
http://deeplearning.net/tutorial/lstm.html
https://www.researchgate.net/post/What_are_the_deep_learning_models_that_provide_the_best_performance_for_sentence_classification
https://www.linkedin.com/pulse/best-ai-algorithms-sentiment-analysis-muktabh-mayank
http://ruder.io/deep-learning-nlp-best-practices/
Hi Ammara,
LSTM in its core, preserves information from inputs that has already passed through it using the hidden state. Unidirectional LSTM only preserves information of the past because the only inputs it has seen are from the past.
Using bidirectional will run your inputs in two ways, one from past to future and one from future to past and what differs this approach from unidirectional is that in the LSTM that runs backwards you preserve information from the future and using the two hidden states combined you are able in any point in time to preserve information from both past and future. What they are suited for is a very complicated question but BiLSTMs show very good results as they can understand context better.
Taking an example :
Let’s say we try to predict the next word in a sentence, on a high level what a unidirectional LSTM will see is
The boys went to ....
And will try to predict the next word only by this context, with bidirectional LSTM you will be able to see information further down the road for example
Forward LSTM:
The boys went to ...
Backward LSTM:
... and then they got out of the pool
You can see that using the information from the future it could be easier for the network to understand what the next word is. This concept of looking into past and future will be helpful when doing sentiment analysis.
Thanks @ Annu Sachan and @Nam Le for the answers. Really help me a lot.
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