#夏日挑战赛# FFH从零开始的鸿蒙机器学习之旅-NLP情感分析

天下之事常成于困约，而败于奢靡。这篇文章主要讲述#夏日挑战赛# FFH从零开始的鸿蒙机器学习之旅-NLP情感分析相关的知识，希望能为你提供帮助。
[本文正在参加星光计划3.0-夏日挑战赛]
1.2 导入Standford CoreNLP库
1.2.1我们可以在官网下载工具包StandfordCoreNLP【#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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1.2.2解压，并引入lib中

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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; publicvoid 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@Override
br/>@Override
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; privatestaticfinal 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 = https://www.songbingjia.com/android/; var action = ; action.bundleName ="com.yzj.card"; action.abilityName = "com.example.nlpdemo.NLPServiceAbility"; action.messageCode = code; action.data = https://www.songbingjia.com/android/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 = https://www.songbingjia.com/android/; 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> < textstyle="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）[想了解更多关于开源的内容，请访问：](https://ost.51cto.com/#bkwz)[51CTO 开源基础软件社区](https://ost.51cto.com#bkwz)https://ost.51cto.com/#bkwz