diff --git a/README b/README
index 6340f9a..7d89080 100644
--- a/README
+++ b/README
@@ -1,6 +1,14 @@
 基于自然语言处理的情感分析工具
 本程序依赖data目录下面的data.zip,先解压缩  data 目录下面的 data.zip到当前目录。
 
+
+
+*新增说明1：2015-04-10测试了不用中文分词器，分词之后 LingPipe 情感分类的准确率，同时测试了去除停用词之后的情感分类的准确率。
+1) 发现用HanLP的NLPTokenizer分词器，准确率最高，但是速度有点慢。
+2) 如果用HanLP的标准分词器就会准确率低一点点，但是速度快。
+3) 分词之后去除停用词效果更加差。
+4) 结巴分词效果不好，而且速度慢。
+
 1、基于词典和贝叶斯模型的情感分析
 主程序：eshore.cn.it.sentiment.Sentiment 此类通过
 data/Sentiment_Dictionary中的正负面词语建立模型。
diff --git a/data/.gitignore b/data/.gitignore
index 31367df..e375520 100644
--- a/data/.gitignore
+++ b/data/.gitignore
@@ -2,4 +2,6 @@
 nerws_corpus/
 output/
 polarity_corpus/
-Sentiment_Dictionary/
\ No newline at end of file
+Sentiment_Dictionary/
+dictionary/
+model/
\ No newline at end of file
diff --git a/pom.xml b/pom.xml
index 66850e2..aa67952 100644
--- a/pom.xml
+++ b/pom.xml
@@ -16,6 +16,7 @@
     <dom4j.version>1.6.1</dom4j.version>
     <lingpipe.version>4.1.0</lingpipe.version>
     <jieba.version>1.0.0</jieba.version>
+    <hanlp.version>1.1.0</hanlp.version>
     <!-- 无法到中央仓库下载的jar包就集中存放到这个位置  -->
     <maven.libs.home>F:/java_git_projects/nlp-sentiment/libs</maven.libs.home>
   </properties>
@@ -54,5 +55,12 @@
 		<artifactId>jieba-analysis</artifactId>
 		<version>${jieba.version}</version>
 	</dependency>
+	
+	
+	<dependency>
+		<groupId>com.hankcs</groupId>
+		<artifactId>hanlp</artifactId>
+		<version>${hanlp.version}</version>
+	</dependency>
   </dependencies>
 </project>
diff --git a/src/main/java/eshore/cn/it/sentiment/ChinesePolarityBasic.java b/src/main/java/eshore/cn/it/sentiment/ChinesePolarityBasic.java
index d1100a9..e3429cc 100644
--- a/src/main/java/eshore/cn/it/sentiment/ChinesePolarityBasic.java
+++ b/src/main/java/eshore/cn/it/sentiment/ChinesePolarityBasic.java
@@ -7,6 +7,12 @@ import java.util.List;
 
 
 
+
+
+
+
+
+
 import org.apache.commons.io.IOUtils;
 
 import com.aliasi.classify.Classification;
@@ -14,9 +20,13 @@ import com.aliasi.classify.Classified;
 import com.aliasi.classify.DynamicLMClassifier;
 import com.aliasi.lm.NGramProcessLM;
 import com.aliasi.util.Files;
-import com.huaban.analysis.jieba.JiebaSegmenter;
-import com.huaban.analysis.jieba.JiebaSegmenter.SegMode;
-import com.huaban.analysis.jieba.SegToken;
+
+import com.hankcs.hanlp.HanLP;
+import com.hankcs.hanlp.seg.common.Term;
+
+//下面的分词器准确率最高，去除停用词反而准确率不高了。
+//import com.hankcs.hanlp.tokenizer.NLPTokenizer;
+
 
 /**
  * ChinesePolarityBasic 此类是利用lingpipe作中文情感预测的示例类
@@ -40,10 +50,7 @@ public class ChinesePolarityBasic {
 	private static final String TESTFILES_INFO = 
 			"data/polarity_corpus/hotel_reviews/test2.rlabelclass"; 
 	private static final String ENCODING = "GBK";
-	
-	private final JiebaSegmenter jiebaSegmenter = new JiebaSegmenter();
-	private final SegMode segMode = SegMode.INDEX;
-	
+
 	public static void main(String[] args) {
 		try {
 			new ChinesePolarityBasic().run();
@@ -65,7 +72,11 @@ public class ChinesePolarityBasic {
 
 	private void run() throws ClassNotFoundException,
     	IOException {
+		
+		//训练数据
 		train();
+		
+		//测试训练结果
 		evaluate();
 	}
 
@@ -105,14 +116,14 @@ public class ChinesePolarityBasic {
 		String review = Files.readFromFile(trainFile, fileEncoding);
 		
 		//此处加入中文分词器，得到分词之后的字符串
-		List<SegToken> segTokens = jiebaSegmenter.process(review, segMode);
-		review = "";
-		for (SegToken seg : segTokens) {
-			review += seg.word.getToken() + " ";
-		}
 		
+		String segWords = "";
+		List<Term> terms = HanLP.segment(review);
+		for (Term term : terms)
+			segWords += term.word + " ";
+   
 		Classified<CharSequence> classified
-			= new Classified<CharSequence>(review,classification);
+			= new Classified<CharSequence>(segWords,classification);
 		mClassifier.handle(classified);
 	}
 	
@@ -126,15 +137,13 @@ public class ChinesePolarityBasic {
 	    String review
         	= Files.readFromFile(testFile, fileEncoding);
 	    //同理，这里可以加入分词器，这样可以试试效果如何。
-	    List<SegToken> segTokens = jiebaSegmenter.process(review, segMode);
-		review = "";
-		for (SegToken seg : segTokens) {
-			review += seg.word.getToken() + " ";
-		}
-	    
+	    String segWords = "";
+	    List<Term> terms = HanLP.segment(review);
+		for (Term term : terms)
+			segWords += term.word + " ";
 	    ++numTests;
 	    Classification classification
-        	= mClassifier.classify(review);
+        	= mClassifier.classify(segWords);
 	    //得到训练结果
 	    String resultCategory
         	= classification.bestCategory();