Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

Algorithm 1

Text sentiment analysis algorithm based on W-RNN.

Input:
CWE-word vector
CTR-training corpus
CTE-test corpus
Output: Prediction results of test samples.
(1)	pro_processing (CWE)
(2)	Dict = word2vec (CWE)//create the word vector dictionary Dict
(3)	batches [] ⟵ Divide (CTR)//divide CTR into several batches
(4)	for i ⟵ 0 to epochs do
(5)	for j ⟵ 0 to length (batches) do
(6)	for k ⟵ 0 to length (batches[j]) do
(7)	⟵ FindWord (batches [j][k])//find the words vector in batches[j][k] from Dict
(8)	h ⟵ //the feature vector h is extracted from
(9)	h′ ⟵ Measure (h)//measure the impact of h
(10)	⟵ Sort (, h’)//sort words vector in descending order according to h’
(11)	c ⟵ ExtractFeature ()//extract secondary feature from the word vector
(12)	z ⟵ Softmax (c)//Get the prediction results of samples by Softmax classifier
(13)	end for
(14)	Update (z, , (b)//update parameters and b of the model by backpropagation
(15)	end for
(16)	end for
(17)	for i ⟵ 0 to length (CTE) do
(18)	⟵ FindWord (CTE [i])
(19)	h ⟵
(20)	h’ ⟵ Measure (h)
(21)	⟵ Sort (, h’)
(22)	c ⟵ ExtractFeature ()
(23)	output ⟵ Softmax (c)
(24)	end for