#夏日挑战赛# FFH从零开始的鸿蒙机器学习之旅-NLP情感分析
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1.2 导入Standford CoreNLP库
1.2.1我们可以在官网下载工具包StandfordCoreNLP
1.2.2解压，并引入lib中
右键文件夹，点击add as library
2.情感分析
2.1 新建JAVA类，NLP_EMOTION
package com.example.nlpdemo.utils;
import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.util.CoreMap;
import java.util.Properties;
public class NLP_EMOTION
//必要： 功能入口
StanfordCoreNLP pipeline = null;
//无关要素 记分用的
public int score;
public void startengine()
//实例化一个对象
Properties props = new Properties();
this.score=0;
//设置所需要的功能，分词，情感分析等，annotators就是前文提到的工具类
props.setProperty("annotators", "tokenize, ssplit, parse, sentiment");
//实现接口
pipeline = new StanfordCoreNLP(props);
public int getScore()
return score;
public String sentiment_emotion(String text)
int emotion;
this.score = 0;
String emotion_state;
String str="";
//传入我们需要分析的字符串
Annotation annotation = pipeline.process(text);
int i=0;
for(CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class))
//语法树
Tree tree = sentence.get(SentimentCoreAnnotations.SentimentAnnotatedTree.class);
//情感打分
emotion = RNNCoreAnnotations.getPredictedClass(tree);
i++;
score+=emotion;
//情感状态
emotion_state = sentence.get(SentimentCoreAnnotations.SentimentClass.class);
str +=emotion_state + ": " + sentence+ " "+emotion +"|";
score = score/i;
return str;
import com.example.nlpdemo.utils.NLP_EMOTION;
import ohos.aafwk.ability.Ability;
import ohos.aafwk.content.Intent;
import ohos.app.Context;
import ohos.hiviewdfx.HiLog;
import ohos.hiviewdfx.HiLogLabel;
import ohos.rpc.*;
import ohos.utils.zson.ZSONObject;
import java.util.HashMap;
import java.util.Map;
public class NLPServiceAbility extends Ability
private static final String TAG = "NLP测试";
// 定义日志标签
private static final HiLogLabel LABEL = new HiLogLabel(3, 0xD000F00, TAG);
private Context context;
private MyRemote remote = new MyRemote();
private String str="";
private IRemoteObject remoteObjectHandler;
static NLP_EMOTION nlpPipeline = null;
private int has_new=0;
// FA在请求PA服务时会调用Ability.connectAbility连接PA，连接成功后，需要在onConnect返回一个remote对象，供FA向PA发送消息br/>@Override
protected IRemoteObject onConnect(Intent intent)
super.onConnect(intent);
return remote.asObject();
public static String test(String s)
String text = s;
nlpPipeline = new NLP_EMOTION();
nlpPipeline.startengine();
String result = nlpPipeline.sentiment_emotion(text);
HiLog.info(LABEL,"yzj"+nlpPipeline.sentiment_emotion(text));
return result;
class MyRemote extends RemoteObject implements IRemoteBroker
private static final int SUCCESS = 0;
private static final int ERROR = 1;
private static final int PLUS = 1001;
private static final int SUBSCRIBE=1005;
private static final int NLP =1010;
MyRemote()
super("MyService_MyRemote");
@Override
public boolean onRemoteRequest(int code, MessageParcel data, MessageParcel reply, MessageOption option)
switch (code)
case SUBSCRIBE:
// 如果仅支持单FA订阅，可直接覆盖：remoteObjectHandler = data.readRemoteObject();
remoteObjectHandler=data.readRemoteObject();
// startNotify();
Map<String, Object> result = new HashMap<String, Object>();
result.put("code", SUCCESS);
reply.writeString(ZSONObject.toZSONString(result));
break;
case PLUS:
String dataStr = data.readString();
// 返回结果当前仅支持String，对于复杂结构可以序列化为ZSON字符串上报
Map<String, Object> result = new HashMap<String, Object>();
result.put("code", SUCCESS);
result.put("abilityResult", "111");
reply.writeString(ZSONObject.toZSONString(result));
break;
case NLP:
str = data.readString();
// 返回结果当前仅支持String，对于复杂结构可以序列化为ZSON字符串上报
HiLog.info(LABEL,str);
Map<String, Object> result = new HashMap<String, Object>();
result.put("code", SUCCESS);
result.put("abilityResult", "NLP函数成功被调用");
result.put("emotion", test(str));
result.put("score",nlpPipeline.getScore());
str="";
reply.writeString(ZSONObject.toZSONString(result));
break;
default:
Map<String, Object> result = new HashMap<String, Object>();
result.put("abilityError", ERROR);
reply.writeString(ZSONObject.toZSONString(result));
return false;
return true;
@Override
public IRemoteObject asObject()
return this;
### 3.2 JS侧
+ index.js
```javascript
export default
data:
title: "",
str:"NONE",
inputfield:"nothing",
tips:"none",
score:"0",
,
onInit()
this.title = "测测你现在的心情";
this.Subscribekv();
this.NLP();
,
//订阅PA
initAction: function (code)
var actionData =
;
var action = ;
action.bundleName = "com.yzj.card";
action.abilityName = "com.example.nlpdemo.NLPServiceAbility";
action.messageCode = code;
action.data = actionData;
action.abilityType = 0;
action.syncOption = 0;
return action;
,
Subscribekv:async function()
try
var action = this.initAction(1005);
var that = this;
var _data = ;
var result = await FeatureAbility.subscribeAbilityEvent(action,function (res) //调用订阅服务API
console.info(" 订阅PA返回的结果是: " + res);
console.info("收到返回结果")
this.onShow();
);
console.info(" subscribeCommonEvent result = " + result);
catch (pluginError)
console.error("subscribeCommonEvent error : result= " + JSON.stringify(pluginError));
,
NLP: async function()
var actionData = ;
actionData=this.str;
var action = ;
action.bundleName = com.yzj.card;
action.abilityName = com.example.nlpdemo.NLPServiceAbility;
action.messageCode = 1010;
action.data = actionData;
action.abilityType = 0;
action.syncOption =0;
var result = await FeatureAbility.callAbility(action);
var ret = JSON.parse(result);
if (ret.code == 0)
console.info(plus result is: + JSON.stringify(ret.abilityResult));
console.info(NLP返回结果+JSON.stringify(ret.emotion));
var ss = JSON.stringify(ret.emotion).replace("|","\\n");
this.inputfield = ss;
console.info("平均emotion:"+JSON.stringify(ret.score));
let rank = parseInt(JSON.stringify(ret.score));
this.score = rank;
if(rank==1)
this.tips="今天或许有些糟糕？";
else if(rank==2)
this.tips = "平平淡淡才是真"
else if(rank>=3)
this.tips ="今天充满欢喜！"
else
console.error(plus error code: + JSON.stringify(ret.code));
,
textfield(e)
this.str=e.value;
-
index.hml
<div class="container"> <text class="title" style="font-size: 32px;"> title </text> <input id="infield" type="text" style="width:70%;height: 12%;font-size: 20px;margin-top: 30px;"@change="textfield" > 请输入文本 </input> <button type="capsule" onclick="NLP" style="width: 150px;height: 60px;margin-top: 30px;"> 测一测 </button> <text style="width: 312px;height: 200px;background-color:cornflowerblue;margin-top: 30px;border-radius: 25px;font-size: 20px;"> inputfield </text> <text style="font-size:20px;width:80%;height:10%;background-color: aquamarine;margin-top: 30px;border-radius: 25px;"> tips 评分 score </text> </div>
## 4.结语
关于机器学习内容还有非常多有意思的事情，这样的模式显然不是最佳的开发模式，5G大的工程文件(哈哈)，最好能部署在云端，只能说实现一些功能，但非好用的功能，却也是一次尝试。在这个包下能够开发出很多有意思的功能，也支持中文等多种语言工具，还可以结合华为鸿蒙目前支持的AI功能，欢迎读者尝试和积极沟通。
**或许，我们应该做一些更大胆的尝试？在HarmonyOS，OpenHarmony上从零搭建机器学习模型，再结合分布式能力，穷尽N多台设备的算力？也不知道手上的麒麟990能到何种程度。**（嘻）
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