SpringBoot项目通过分词器生成词云
目录
- 前言
- 一、词云是什么？
- 二、使用步骤
- 1.引入依赖
- 2.application.yml
- 3.Controller
- 4.分词工具类
- 4.词云生成工具类、支持输出文件和字节流
- 注意
前言
公司项目涉及到员工任务管理，需要从员工任务中获取任务信息生成个人词云图，可以把员工任务中较为高频的词语突出展示。
一、词云是什么？
词云就是对文本中出现频率较高的“关键词”予以视觉上的突出，形成“关键词云层” 或“关键词渲染”，从而过滤掉大量的文本信息，使浏览网页者只要一眼扫过文本就可以领略文本的主旨。
二、使用步骤
1.引入依赖
<!-- IK分词器 -->
<dependency><groupId>cn.shenyanchao.ik-analyzer</groupId><artifactId>ik-analyzer</artifactId><version>9.0.0</version>
</dependency><!-- 詞雲 -->
<dependency><groupId>com.kennycason</groupId><artifactId>kumo-core</artifactId><version>1.28</version>
</dependency><dependency><groupId>com.kennycason</groupId><artifactId>kumo-tokenizers</artifactId><version>1.28</version>
</dependency><!-- web -->
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId>
</dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><optional>true</optional>
</dependency>
2.application.yml
server:port: 8088# 关闭日志输出 （可选）
logging:level:com.kennycason.kumo.WordCloud: OFF
3.Controller
import com.chendi.mydemo.utils.IkAnalyzerUtils;
import com.chendi.mydemo.utils.WorkCloudUtil;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;import java.util.ArrayList;
import java.util.List;
import java.util.Map;@RestController
public class TestController {@GetMapping("/")public void test() {List<String> list = new ArrayList<>();list.add("爱购物，爱手机，爱电脑，爱上网");list.add("爱学习，爱游戏，爱吃饭，爱睡觉");list.add("爱上班，爱下班，爱加班，爱翘班");list.add("爱上班，爱下班，爱加班，爱翘班");list.add("夏天的阳光明媚灿烂，\n" +"大自然万物生机盎然。\n" +"清晨的微风吹过花丛，\n" +"点缀着青草和蓝天。\n" +"\n" +"蝴蝶翩翩起舞在花间，\n" +"蜜蜂忙碌采集甘甜。\n" +"鸟儿欢快地歌唱着，\n" +"为夏日带来欢欣和欢愉。\n" +"\n" +"海浪轻拍沙滩起伏，\n" +"沙粒细腻温热宜走。\n" +"阳光透过水面璀璨，\n" +"让海洋如银河般流动。\n" +"\n" +"夏日的夜晚星空闪耀，\n" +"月亮洒下银色光晕。\n" +"夏虫的音符演奏着，\n" +"营造出夏夜的美妙。\n" +"\n" +"夏天啊，你是如此迷人，\n" +"给人们带来快乐和欢欣。\n" +"在你的怀抱里，我们尽情享受，\n" +"夏天，你是美丽的季节！");Map<String, Integer> wordMap = IkAnalyzerUtils.wordCloud(list, 0);WorkCloudUtil.generateWriteImage(wordMap);}}
4.分词工具类
import org.wltea.analyzer.core.IKSegmenter;
import org.wltea.analyzer.core.Lexeme;import java.io.IOException;
import java.io.StringReader;
import java.util.*;/*** 解析工具类*/
public class IkAnalyzerUtils {/*** 拆分词云** @param list 需要拆分的词云集合* @param quantity 结果集取的数量*/public static String wordCloudParsing(List<String> list, Integer quantity) {Map<String,Integer> result = wordCloud(list,quantity);StringBuilder str = new StringBuilder();result.forEach((k, v) -> {String value = " " + k;str.append(value);});return str.toString().trim();}/*** 拆分词云** @param list 需要拆分的词云集合* @param quantity 结果集取的数量*/public static List<Map<String,Object>> wordCloudList(List<String> list, Integer quantity) {Map<String,Integer> result = wordCloud(list,quantity);List<Map<String,Object>> mapList = new LinkedList<>();result.forEach((k, v) -> {Map<String,Object> map = new HashMap<>(16);map.put("name",k);map.put("value",v);mapList.add(map);});Collections.reverse(mapList);return mapList;}/*** 拆分词云** @param list 需要拆分的词云集合* @param quantity 结果集取的数量*/public static Map<String,Integer> wordCloud(List<String> list, Integer quantity) {StringReader reader = new StringReader(String.join(",", list));IKSegmenter ikSegmenter = new IKSegmenter(reader, true);Map<String, Integer> map = null;try {Lexeme lexeme;map = new HashMap<>(16);while ((lexeme = ikSegmenter.next()) != null) {String str = lexeme.getLexemeText();Integer num = map.get(str);if (num != null && num > 0) {map.put(str, num + 1);} else {map.put(str, 1);}}reader.close();} catch (IOException e) {e.printStackTrace();}Map<String, Integer> result = new LinkedHashMap<>();if (quantity != null && quantity > 0) {map.entrySet().stream().sorted(Map.Entry.comparingByValue()).limit(quantity).forEachOrdered(item -> result.put(item.getKey(), item.getValue()));} else {map.entrySet().stream().sorted(Map.Entry.comparingByValue()).forEachOrdered(item -> result.put(item.getKey(), item.getValue()));}return result;}
}4.词云生成工具类、支持输出文件和字节流
import com.kennycason.kumo.CollisionMode;
import com.kennycason.kumo.WordCloud;
import com.kennycason.kumo.WordFrequency;
import com.kennycason.kumo.bg.CircleBackground;
import com.kennycason.kumo.font.KumoFont;
import com.kennycason.kumo.font.scale.SqrtFontScalar;
import com.kennycason.kumo.nlp.FrequencyAnalyzer;
import com.kennycason.kumo.nlp.tokenizers.ChineseWordTokenizer;
import com.kennycason.kumo.palette.ColorPalette;
import lombok.SneakyThrows;import java.awt.*;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;public class WorkCloudUtil {@SneakyThrowspublic static InputStream generateImageStream(Map<String, Integer> wordMap) {WordCloud wordCloud = generateWordCloud(wordMap);//输出字节流ByteArrayOutputStream out =new ByteArrayOutputStream();wordCloud.writeToStreamAsPNG(out);return new ByteArrayInputStream(out.toByteArray());}@SneakyThrowspublic static void generateWriteImage(Map<String, Integer> wordMap) {WordCloud wordCloud = generateWordCloud(wordMap);wordCloud.writeToFile("D:\\chendi\\cd.png");}public static WordCloud generateWordCloud(Map<String, Integer> wordMap){if (wordMap == null || wordMap.size() == 0) {return null;}final FrequencyAnalyzer frequencyAnalyzer = new FrequencyAnalyzer();frequencyAnalyzer.setWordFrequenciesToReturn(600);frequencyAnalyzer.setMinWordLength(2);frequencyAnalyzer.setWordTokenizer(new ChineseWordTokenizer());final List<WordFrequency> wordFrequencies = new ArrayList<>();for (Map.Entry<String, Integer> entry : wordMap.entrySet()) {wordFrequencies.add(new WordFrequency(entry.getKey(), entry.getValue()));}Font font = FontUtil.getFont("/static/fonts/QingNiaoHuaGuangJianMeiHei-2.ttf");//设置图片分辨率final Dimension dimension = new Dimension(400, 400);//此处的设置采用内置常量即可，生成词云对象final WordCloud wordCloud = new WordCloud(dimension, CollisionMode.PIXEL_PERFECT);//设置边界及字体wordCloud.setPadding(2);wordCloud.setBackgroundColor(Color.WHITE);//设置背景图层为圆形，设置圆形的大小wordCloud.setBackground(new CircleBackground(200));//设置词云显示的三种颜色，越靠前设置表示词频越高的词语的颜色wordCloud.setColorPalette(new ColorPalette(new Color(0x4055F1), new Color(0x408DF1), new Color(0x40AAF1), new Color(0x40C5F1), new Color(0x40D3F1), new Color(0xFFFFFF)));//设置字体的大小wordCloud.setFontScalar(new SqrtFontScalar(10, 40));wordCloud.setKumoFont(new KumoFont(font));wordCloud.build(wordFrequencies);//设置背景图片，如果想要固定的形状，就插入这个形状的图片//wordCloud.setBackground(new PixelBoundryBackground("E:\\星星/star.jpg"));return wordCloud;}}注意
处理中文需要宿主机有中文字体包、如果宿主机不支持中文，请下载一个中文字体包
本文指定使用的就是QingNiaoHuaGuangJianMeiHei-2.ttf字体
百度一下、找不到私信我发你QingNiaoHuaGuangJianMeiHei-2.ttf字体包