recurrent neural networks the case of sentiment classification (many-to-one rnn)there are sarcastic comments where the words following after positive negate the presence of the positive word--for example" loved the moviebiggest joke ever!herethe context on the right side nullifies the presence of the word "loved bidirectional rnn can be described using the following equationshb tanh wb bb ft tanh yt softmax vb hbt (ft the rnn computation involves computing the forward hidden state and backward hidden state for an entity in the sequence this is denoted by ft and hb respectively the computation of ft uses the corresponding input at entity (tand the previous hidden state ft the computation of hb uses the corresponding input at entity (tand the previous hidden state hb the output yt is computed using the hidden state ft and hb while computing the current hidden statea set of weights are associated with the input and the previous hidden state this is denoted by uf wf uband wbrespectively there are also bias termsdenoted by bf and bb similarlywhile computing the outputa set of weights are associated with the hidden state while computing the output this is denoted by vb and vf there is also bias termdenoted by the tanh activation function is used in the computation of the hidden state the softmax activation function is used in the computation of the output the rnnas described by the equationscan process an arbitrarily large input sequence the parameters of the rnn--uf ubwf wbvbvf bf bbcetc --are shared across the computation of the hidden layer and output value (for each of entities in the sequence