diff --git a/demo/sentiment/predict.py b/demo/sentiment/predict.py
index 00239c6009b8503cf445d9847abde92db12db2fe..0095c6f7272a2191ea39e042a836f7d6038032aa 100755
--- a/demo/sentiment/predict.py
+++ b/demo/sentiment/predict.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
+import os, sys
 import numpy as np
 from optparse import OptionParser
 from py_paddle import swig_paddle, DataProviderConverter
@@ -66,35 +66,27 @@ class SentimentPrediction():
         for v in open(label_file, 'r'):
             self.label[int(v.split('\t')[1])] = v.split('\t')[0]
 
-    def get_data(self, data_file):
+    def get_index(self, data):
         """
-        Get input data of paddle format.
+        transform word into integer index according to the dictionary.
         """
-        with open(data_file, 'r') as fdata:
-            for line in fdata:
-                words = line.strip().split()
-                word_slot = [
-                    self.word_dict[w] for w in words if w in self.word_dict
-                ]
-                if not word_slot:
-                    print "all words are not in dictionary: %s", line
-                    continue
-                yield [word_slot]
+        words = data.strip().split()
+        word_slot = [
+            self.word_dict[w] for w in words if w in self.word_dict
+        ]
+        return word_slot
 
-    def predict(self, data_file):
-        """
-        data_file: file name of input data.
-        """
-        input = self.converter(self.get_data(data_file))
+    def batch_predict(self, data_batch):
+        input = self.converter(data_batch)
         output = self.network.forwardTest(input)
         prob = output[0]["value"]
-        lab = np.argsort(-prob)
-        if self.label is None:
-            print("%s: predicting label is %d" % (data_file, lab[0][0]))
-        else:
-            print("%s: predicting label is %s" %
-                  (data_file, self.label[lab[0][0]]))
-
+        labs = np.argsort(-prob)
+        for idx, lab in enumerate(labs):
+            if self.label is None:
+                print("predicting label is %d" % (lab[0]))
+            else:
+                print("predicting label is %s" %
+                      (self.label[lab[0]]))
 
 def option_parser():
     usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
@@ -119,11 +111,13 @@ def option_parser():
         default=None,
         help="dictionary file")
     parser.add_option(
-        "-i",
-        "--data",
+        "-c",
+        "--batch_size",
+        type="int",
         action="store",
-        dest="data",
-        help="data file to predict")
+        dest="batch_size",
+        default=1,
+        help="the batch size for prediction")
     parser.add_option(
         "-w",
         "--model",
@@ -137,14 +131,21 @@ def option_parser():
 def main():
     options, args = option_parser()
     train_conf = options.train_conf
-    data = options.data
+    batch_size = options.batch_size
     dict_file = options.dict_file
     model_path = options.model_path
     label = options.label
     swig_paddle.initPaddle("--use_gpu=0")
     predict = SentimentPrediction(train_conf, dict_file, model_path, label)
-    predict.predict(data)
 
+    batch = []
+    for line in sys.stdin:
+        batch.append([predict.get_index(line)])
+        if len(batch) == batch_size:
+            predict.batch_predict(batch)
+            batch=[]
+    if len(batch) > 0:
+        predict.batch_predict(batch)
 
 if __name__ == '__main__':
     main()
diff --git a/demo/sentiment/predict.sh b/demo/sentiment/predict.sh
index a889dfe3ec6635bd1ab2b60ae7207815cd205416..c72a8e8641516543ef267fcb4b448630246d1e8d 100755
--- a/demo/sentiment/predict.sh
+++ b/demo/sentiment/predict.sh
@@ -19,9 +19,9 @@ set -e
 model=model_output/pass-00002/
 config=trainer_config.py
 label=data/pre-imdb/labels.list
-python predict.py \
-     -n $config\
-     -w $model \
-     -b $label \
-     -d ./data/pre-imdb/dict.txt \
-     -i ./data/aclImdb/test/pos/10007_10.txt 
+cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
+     --tconf=$config\
+     --model=$model \
+     --label=$label \
+     --dict=./data/pre-imdb/dict.txt \
+     --batch_size=1
diff --git a/doc/tutorials/sentiment_analysis/index_en.md b/doc/tutorials/sentiment_analysis/index_en.md
index c53952c544de9fa88a6318432e34b0d05b149445..bb7681db44ca6f286ad6935ddfecb9becb429192 100644
--- a/doc/tutorials/sentiment_analysis/index_en.md
+++ b/doc/tutorials/sentiment_analysis/index_en.md
@@ -293,20 +293,21 @@ predict.sh:
 model=model_output/pass-00002/
 config=trainer_config.py
 label=data/pre-imdb/labels.list
-python predict.py \
-     -n $config\
-     -w $model \
-     -b $label \
-     -d data/pre-imdb/dict.txt \
-     -i data/aclImdb/test/pos/10007_10.txt
-```
-
-* `predict.py`: predicting interface.
-*  -n $config : set network configure.
-*  -w $model: set model path.
-*  -b $label: set dictionary about corresponding relation between integer label and string label.
-*  -d data/pre-imdb/dict.txt: set dictionary.
-*  -i data/aclImdb/test/pos/10014_7.txt: set one example file to predict.
+cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
+     --tconf=$config\
+     --model=$model \
+     --label=$label \
+     --dict=./data/pre-imdb/dict.txt \
+     --batch_size=1
+```
+
+* `cat ./data/aclImdb/test/pos/10007_10.txt` : the input sample.
+* `predict.py` : predicting interface.
+* `--tconf=$config` : set network configure.
+* ` --model=$model` : set model path.
+* `--label=$label` : set dictionary about corresponding relation between integer label and string label.
+* `--dict=data/pre-imdb/dict.txt` : set dictionary.
+* `--batch_size=1` : set batch size.
 
 Note you should make sure the default model path `model_output/pass-00002`
 exists or change the model path.
diff --git a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md
index b70f2d59675615c26b29932cdf99d728bb206148..ba307e97e3010629548460e25e894d082a6ddd4e 100644
--- a/doc_cn/demo/sentiment_analysis/sentiment_analysis.md
+++ b/doc_cn/demo/sentiment_analysis/sentiment_analysis.md
@@ -291,20 +291,21 @@ predict.sh:
 model=model_output/pass-00002/
 config=trainer_config.py
 label=data/pre-imdb/labels.list
-python predict.py \
-     -n $config\
-     -w $model \
-     -b $label \
-     -d data/pre-imdb/dict.txt \
-     -i data/aclImdb/test/pos/10007_10.txt
-```
-
-* `predict.py`: 预测接口脚本。
-*  -n $config : 设置网络配置。
-*  -w $model: 设置模型路径。
-*  -b $label: 设置标签类别字典，这个字典是整数标签和字符串标签的一个对应。
-*  -d data/pre-imdb/dict.txt: 设置字典文件。
-*  -i data/aclImdb/test/pos/10014_7.txt: 设置一个要预测的示例文件。
+cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
+     --tconf=$config\
+     --model=$model \
+     --label=$label \
+     --dict=./data/pre-imdb/dict.txt \
+     --batch_size=1
+```
+
+* `cat ./data/aclImdb/test/pos/10007_10.txt` : 输入预测样本。
+* `predict.py` : 预测接口脚本。
+* `--tconf=$config` : 设置网络配置。
+* `--model=$model` : 设置模型路径。
+* `--label=$label` : 设置标签类别字典，这个字典是整数标签和字符串标签的一个对应。
+* `--dict=data/pre-imdb/dict.txt` : 设置字典文件。
+* `--batch_size=1` : 设置batch size。
 
 注意应该确保默认模型路径`model_output / pass-00002`存在或更改为其它模型路径。