e1  res = 0.7587996
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 150
attn_length = -1
attention_size = 128
layer_num = 1
forget_bias = 0.5
word(200)、emoji(300)（去除原句表情）

e2  res = 0.76263769
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 150
attn_length = -1
attention_size = 256
layer_num = 1
forget_bias = 0.5
word(200)、emoji(300)（去除原句表情）

e3  res = 0.75979188
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 100
attn_length = -1
attention_size = 256
layer_num = 1
forget_bias = 0.5
word(200)、emoji(200)（去除原句表情）

e4  res = 0.7628608
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 100
attn_length = -1
attention_size = 256
layer_num = 1
forget_bias = 0.5
word(200)、emoji(200)（保留原句表情）

s1  res = 0.762191228
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 100
attn_length = -1
attention_size = 256
layer_num = 1
forget_bias = 0.5
word(200)、swear(200)（保留原句表情）

es1  res = 0.75788377
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 100
attn_length = -1
attention_size = 256
layer_num = 1
forget_bias = 0.5
word(200)、emoji(200)、swear(200)（去除原句表情）

es2  res = 0.75757969
input_keep_prob_ = 0.5
output_keep_prob_ = 0.5
lstmUnitNum = 100
attn_length = -1
attention_size = 256
layer_num = 1
forget_bias = 0.5
word(200)、emoji(200)、swear(200)（保留原句表情）

0
（表情向量单独表示）