推荐|Java 方式实现词云显示

Java 方式实现词云显示

25-03-07 20:01

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之前做文本数据的机器学习分类，需要对文本进行分词、词云分析，为了更好的呈现分词效果，使用了JAVA来生成词云图。说不定能帮上你~

先来看下词云效果图吧！

实现词云图2种方式`word_cloud、stylecloud`

word_cloud ： http://amueller.github.io/word_cloud/

stylecloud ：https://github.com/minimaxir/stylecloud

接下来，是通过调用WordCloud来实现词云的显示

1、需要先添加一下maven依赖

 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="1">

class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="1"> class="hljs-ln-code"> class="hljs-ln-line">package com.lei.test; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="2"> class="hljs-ln-code"> class="hljs-ln-line"> class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="3"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.CollisionMode; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="4"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.WordCloud; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="5"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.WordFrequency; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="6"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.bg.CircleBackground; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="7"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.font.KumoFont; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="8"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.font.scale.SqrtFontScalar; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="9"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.nlp.FrequencyAnalyzer; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="10"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.nlp.tokenizers.ChineseWordTokenizer; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="11"> class="hljs-ln-code"> class="hljs-ln-line">import com.kennycason.kumo.palette.LinearGradientColorPalette; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="12"> class="hljs-ln-code"> class="hljs-ln-line"> class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="13"> class="hljs-ln-code"> class="hljs-ln-line">import java.awt.*; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="14"> class="hljs-ln-code"> class="hljs-ln-line">import java.util.*; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="15"> class="hljs-ln-code"> class="hljs-ln-line">import java.util.List; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="16"> class="hljs-ln-code"> class="hljs-ln-line"> class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="17"> class="hljs-ln-code"> class="hljs-ln-line"> class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="18"> class="hljs-ln-code"> class="hljs-ln-line">public class T00_WordCountSample { class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="19"> class="hljs-ln-code"> class="hljs-ln-line"> public static void main(String[] args) { class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="20"> class="hljs-ln-code"> class="hljs-ln-line"> createWordCountPic(); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="21"> class="hljs-ln-code"> class="hljs-ln-line"> } class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="22"> class="hljs-ln-code"> class="hljs-ln-line"> class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="23"> class="hljs-ln-code"> class="hljs-ln-line"> private static void createWordCountPic(){ class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="24"> class="hljs-ln-code"> class="hljs-ln-line"> FrequencyAnalyzer frequencyAnalyzer = new FrequencyAnalyzer(); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="25"> class="hljs-ln-code"> class="hljs-ln-line"> frequencyAnalyzer.setWordFrequenciesToReturn(600); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="26"> class="hljs-ln-code"> class="hljs-ln-line"> frequencyAnalyzer.setMinWordLength(2); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="27"> class="hljs-ln-code"> class="hljs-ln-line"> frequencyAnalyzer.setWordTokenizer(new ChineseWordTokenizer()); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="28"> class="hljs-ln-code"> class="hljs-ln-line"> // 可以直接从文件中读取 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="29"> class="hljs-ln-code"> class="hljs-ln-line"> //List wordFrequencies = frequencyAnalyzer.load(getInputStream("D:\\citydo-one\\技术\\Java_Note-master\\python\\tp\\Trump.txt")); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="30"> class="hljs-ln-code"> class="hljs-ln-line"> List wordFrequencies = new ArrayList<>(); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="31"> class="hljs-ln-code"> class="hljs-ln-line"> // 用词语来随机生成词云 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="32"> class="hljs-ln-code"> class="hljs-ln-line"> List test = Arrays.asList("你好","谢谢"); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="33"> class="hljs-ln-code"> class="hljs-ln-line"> String strValue = "铁路投资=20, 步伐=20, 国际证券=10, 新开工=15, 加快=15, 逆势增长=15, 增速=7, 万亿元=7, 5月份=6, 同增=5, 水利=5, 相比=5, 跌幅=5, 公路=4, 建筑行业=4, 基建=4, 投资额=4, 本周=4, 高铁=4, 铁路=4, 市政=4, 项目=4, 板块=4, 减少=3, 表现=3, 铁路建设=3, 亿元=3, 新线=3, 回落=3, 开通=3, 下跌=3, 中标=3, 上市公司=3, 6月份=3, 细分=2, 六保=2, 三大=2, 单相=2, 单月=2, 六稳=2, 建筑板块=2, 2项=2, 0.11=2, 洪涝灾害=2, 增长=2, 基建投资=2, 全国铁路=2, 最小=2, 投资=2, 作用=2, 稳步复苏=2, 行业=2, 相关=2, 提升=2, 地产=2, 火车头=2, 预计=2, 央企=2, 建筑=2, 施工=2, 来源=2, 地产投资=2, 签订=2, 比上周=2, 区间=2, 不及预期=2, 34.6=2, 中的=2, 8.4=2, 同比分别=2, 大幅提升=1, 持续=1, 建设步伐=1, 环比=1, 口径=1, 115=1, 其次是=1, 公布=1, 当月=1, 建筑指数=1, 24个=1, 2.85=1, 据介绍=1, 2020年=1, 产业链=1, 行业投资=1, 投资增速=1, 10.8=1, 较上周=1, 亮眼=1, 电热=1, 多个=1, 29.2=1, 欠佳=1, 装配式建筑=1, 收盘=1, ppp=1, 总体=1, 复工=1, 建中=1, 中国=1, 6.3=1, 6.5=1, 下挫=1, 发行=1, 0.07=1, 合计=1, 跑赢大盘=1, 稳步增长=1, 大盘=1, 下半年=1, 3项=1, 计为=1, 气水=1, 0.14=1, 好于=1, 概念板块=1, 金融界=1, 分别为=1, 专项=1, 明显加快=1, 上涨=1, 订单金额=1, 上半年=1, 增速放缓=1, 0.44=1, 开工=1, 1.08=1, 超过=1, 30.1=1, 整体估值=1, 4.5=1, 1.8pct=1, 0.020=1, 八大=1, 1178=1, epc=1, 主要风险=1, 0.60=1, 公用=1, 申万=1, 连镇=1, 2084.43=1, 显现=1, 有望=1, 跌幅为=1, 投资数据=1, 合并=1, 改善=1, 复苏=1, 0.87=1, 0.950=1, 0.85=1, 新开工项目=1, 评级=1, 北段=1, 影响=1, 房地产开发投资=1, 影响下=1, 主要是=1, 0.90=1, 园林工程=1, 2.36=1, 1.68=1, 0.93=1, 涨幅=1, 二级=1, 6月=1, 建筑节能=1, 建筑工程=1, 情况=1, 交运=1, 四大=1, 网站=1, 下降=1, 邮储=1, 开始显现=1, 设计=1, 格库铁路=1, 12.1=1, 2.39=1, 微涨=1, 3.05=1, 所致=1, 继续保持=1, 5000=1, 认为是=1, 3.19=1, 4400公里=1, 新疆=1, 2300公里=1, 订单=1, 分支=1, 公里=1"; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="34"> class="hljs-ln-code"> class="hljs-ln-line"> String[] split = strValue.split(", "); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="35"> class="hljs-ln-code"> class="hljs-ln-line"> String word = ""; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="36"> class="hljs-ln-code"> class="hljs-ln-line"> int count = 0; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="37"> class="hljs-ln-code"> class="hljs-ln-line"> class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="38"> class="hljs-ln-code"> class="hljs-ln-line"> for (int i = 0; i < split.length; i++) { class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="39"> class="hljs-ln-code"> class="hljs-ln-line"> String[] wordInfo = split[i].split("="); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="40"> class="hljs-ln-code"> class="hljs-ln-line"> word = wordInfo[0]; class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="41"> class="hljs-ln-code"> class="hljs-ln-line"> count = Integer.valueOf(wordInfo[1]); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="42"> class="hljs-ln-code"> class="hljs-ln-line"> wordFrequencies.add(new WordFrequency(word, count)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="43"> class="hljs-ln-code"> class="hljs-ln-line"> } class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="44"> class="hljs-ln-code"> class="hljs-ln-line"> //加入分词并随机生成权重，每次生成得图片都不一样 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="45"> class="hljs-ln-code"> class="hljs-ln-line"> //test.stream().forEach(e-> wordFrequencies.add(new WordFrequency(e,new Random().nextInt(test.size())))); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="46"> class="hljs-ln-code"> class="hljs-ln-line"> //此处不设置会出现中文乱码 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="47"> class="hljs-ln-code"> class="hljs-ln-line"> java.awt.Font font = new java.awt.Font("STSong-Light", 2, 18); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="48"> class="hljs-ln-code"> class="hljs-ln-line"> //设置图片分辨率 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="49"> class="hljs-ln-code"> class="hljs-ln-line"> Dimension dimension = new Dimension(500, 500); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="50"> class="hljs-ln-code"> class="hljs-ln-line"> //此处的设置采用内置常量即可，生成词云对象 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="51"> class="hljs-ln-code"> class="hljs-ln-line"> WordCloud wordCloud = new WordCloud(dimension, CollisionMode.PIXEL_PERFECT); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="52"> class="hljs-ln-code"> class="hljs-ln-line"> //设置边界及字体 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="53"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setPadding(2); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="54"> class="hljs-ln-code"> class="hljs-ln-line"> //因为我这边是生成一个圆形,这边设置圆的半径 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="55"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setBackground(new CircleBackground(255)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="56"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setFontScalar(new SqrtFontScalar(12, 42)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="57"> class="hljs-ln-code"> class="hljs-ln-line"> //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 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="58"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setColorPalette(new LinearGradientColorPalette(Color.RED, Color.BLUE, Color.GREEN, 30, 30)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="59"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setKumoFont(new KumoFont(font)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="60"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setBackgroundColor(new Color(255, 255, 255)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="61"> class="hljs-ln-code"> class="hljs-ln-line"> //因为我这边是生成一个圆形,这边设置圆的半径 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="62"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.setBackground(new CircleBackground(255)); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="63"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.build(wordFrequencies); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="64"> class="hljs-ln-code"> class="hljs-ln-line"> //生成词云图路径 class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="65"> class="hljs-ln-code"> class="hljs-ln-line"> wordCloud.writeToFile("E:\\wordCount.png"); class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="66"> class="hljs-ln-code"> class="hljs-ln-line"> } class="hljs-ln-numbers"> class="hljs-ln-line hljs-ln-n" data-line-number="67"> class="hljs-ln-code"> class="hljs-ln-line">} class="hide-preCode-box">

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之前做文本数据的机器学习分类，需要对文本进行分词、词云分析，为了更好的呈现分词效果，使用了JAVA来生成词云图。说不定能帮上你~

实现词云图2种方式word_cloud、stylecloud

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实现词云图2种方式`word_cloud、stylecloud`