数据挖掘：基于Spark+HanLP实现影视评论关键词抽取(1)

1. 背景

近日项目要求基于爬取的影视评论信息，抽取影视的关键字信息。考虑到影视评论数据量较大，因此采用Spark处理框架。关键词提取的处理主要包含分词+算法抽取两部分。目前分词工具包较为主流的，包括哈工大的LTP以及HanLP，而关键词的抽取算法较多，包括TF-IDF、TextRank、互信息等。本次任务主要基于LTP、HanLP、Ac双数组进行分词，采用TextRank、互信息以及TF-IDF结合的方式进行关键词抽取。

说明：本项目刚开始接触，因此效果层面需迭代调优。

2. 技术选型

(1) 词典

1) 基于HanLP项目提供的词典数据，具体可参见HanLP的github。

2) 考虑到影视的垂直领域特性，引入腾讯的嵌入的汉语词，参考该地址。

(2) 分词

1) LTP分词服务：基于Docker Swarm部署多副本集服务，通过HTTP协议请求，获取分词结果(部署方法可百度); 也可以直接在本地加载，放在内存中调用，效率更高(未尝试)

2) AC双数组：基于AC双数组，采用最长匹配串，采用HanLP中的AC双数组分词器

(3) 抽取

1) 经典的TF-IDF：基于词频统计实现

2) TextRank：借鉴于PageRank算法，基于HanLP提供的接口

3) 互信息：基于HanLP提供的接口

3. 实现代码

(1) 代码结构

1) 代码将分词服务进行函数封装，基于不同的名称，执行名称指定的分词

2) TextRank、互信息、LTP、AC双数组等提取出分词或短语，最后均通过TF-IDF进行统计计算

(2) 整体代码

1) 主体代码：细节层面与下载的原始评论数据结构有关，因此无需过多关注，只需关注下主体流程即可

def extractFilmKeyWords(algorithm: String): Unit ={
    // 测试
	println(HanLPSpliter.getInstance.seg("如何看待《战狼2》中的爱国情怀？"))

    val sc = new SparkContext(new SparkConf().setAppName("extractFileKeyWords").set("spark.driver.maxResultSize", "3g"))

    val baseDir = "/work/ws/video/parse/key_word"

    import scala.collection.JavaConversions._
    def extractComments(sc: SparkContext, inputInfo: (String, String)): RDD[(String, List[String])] = {
      sc.textFile(s"$baseDir/data/${inputInfo._2}")
        .map(data => {
          val json = JSONObjectEx.fromObject(data.trim)
          if(null == json) ("", List())
          else{
            val id = json.getStringByKeys("_id")
            val comments: List[String] = json.getArrayInfo("comments", "review").toList
            val reviews: List[String] = json.getArrayInfo("reviews", "review").toList
            val titles: List[String] = json.getArrayInfo("reviews", "title").toList
            val texts = (comments ::: reviews ::: titles).filter(f => !CleanUtils.isEmpty(f))
            (IdBuilder.getSourceKey(inputInfo._1, id), texts)
          }
        })
    }

    // 广播停用词
    val filterWordRdd = sc.broadcast(sc.textFile(s"$baseDir/data/stopwords.txt").map(_.trim).distinct().collect().toList)

    def formatOutput(infos: List[(Int, String)]): String ={
      infos.map(info => {
        val json = new JSONObject()
        json.put("status", info._1)
        try{
          json.put("res", info._2)
        } catch {
          case _ => json.put("res", "[]")
        }
        json.toString.replaceAll("[\s]+", "")
      }).mkString(" | ")
    }

    def genContArray(words: List[String]): JSONArray ={
      val arr = new JSONArray()
      words.map(f => {
        val json = new JSONObject()
        json.put("cont", f)
        arr.put(json)
      })
      arr
    }

	// 基于LTP分词服务
    def splitWordByLTP(texts: List[String]): List[(Int, String)] ={
      texts.map(f => {
        val url = "http://dev.content_ltp.research.com/ltp"
        val params = new util.HashMap[String, String]()
        params.put("s", f)
        params.put("f", "json")
        params.put("t", "ner")
        // 调用LTP分词服务
        val result = HttpPostUtil.httpPostRetry(url, params).replaceAll("[\s]+", "")
        if (CleanUtils.isEmpty(result)) (0, f) else {
          val resultArr = new JSONArray()

          val jsonArr = try { JSONArray.fromString(result) } catch { case _ => null}
          if (null != jsonArr && 0 < jsonArr.length()) {
            for (i <- 0 until jsonArr.getJSONArray(0).length()) {
              val subJsonArr = jsonArr.getJSONArray(0).getJSONArray(i)
              for (j <- 0 until subJsonArr.length()) {
                val subJson = subJsonArr.getJSONObject(j)
                if(!filterWordRdd.value.contains(subJson.getString("cont"))){
                  resultArr.put(subJson)
                }
              }
            }
          }
          if(resultArr.length() > 0) (1, resultArr.toString) else (0, f)
        }
      })
    }

	// 基于AC双数组搭建的分词服务
    def splitWordByAcDoubleTreeServer(texts: List[String]): List[(Int, String)] ={
      texts.map(f => {
        val splitResults = SplitQueryHelper.splitQueryText(f)
          .filter(f => !CleanUtils.isEmpty(f) && !filterWordRdd.value.contains(f.toLowerCase)).toList
        if (0 == splitResults.size) (0, f) else (1, genContArray(splitResults).toString)
      })
    }

	// 内存加载AC双数组
    def splitWordByAcDoubleTree(texts: List[String]): List[(Int, String)] ={
      texts.map(f => {
        val splitResults =  HanLPSpliter.getInstance().seg(f)
          .filter(f => !CleanUtils.isEmpty(f) && !filterWordRdd.value.contains(f.toLowerCase)).toList
        if (0 == splitResults.size) (0, f) else (1, genContArray(splitResults).toString)
      })
    }

	// TextRank
    def splitWordByTextRank(texts: List[String]): List[(Int, String)] ={
      texts.map(f => {
        val splitResults = HanLP.extractKeyword(f, 100)
          .filter(f => !CleanUtils.isEmpty(f) && !filterWordRdd.value.contains(f.toLowerCase)).toList
        if (0 == splitResults.size) (0, f) else {
          val arr = genContArray(splitResults)
          if(0 == arr.length()) (0, f) else (1, arr.toString)
        }
      })
    }

	// 互信息
    def splitWordByMutualInfo(texts: List[String]): List[(Int, String)] ={
      texts.map(f => {
        val splitResults = HanLP.extractPhrase(f, 50)
          .filter(f => !CleanUtils.isEmpty(f) && !filterWordRdd.value.contains(f.toLowerCase)).toList
        if (0 == splitResults.size) (0, f) else {
          val arr = genContArray(splitResults)
          if(0 == arr.length()) (0, f) else (1, arr.toString)
        }
      })
    }

    // 提取分词信息
    val unionInputRdd = sc.union(
	  extractComments(sc, SourceType.DB -> "db_review.json"),
      extractComments(sc, SourceType.MY -> "my_review.json"),
      extractComments(sc, SourceType.MT -> "mt_review.json"))
      .filter(_._2.nonEmpty)

    unionInputRdd.cache()

    unionInputRdd.map(data => {
      val splitResults = algorithm match {
        case "ltp" => splitWordByLTP(data._2)
        case "acServer" => splitWordByAcDoubleTreeServer(data._2)
        case "ac" => splitWordByAcDoubleTree(data._2)
        case "textRank" => splitWordByTextRank(data._2)
        case "mutualInfo" => splitWordByMutualInfo(data._2)
      }

      val output = formatOutput(splitResults)
      s"${data._1}	$output"
    }).saveAsTextFile(HDFSFileUtil.clean(s"$baseDir/result/wordSplit/$algorithm"))

    val splitRDD = sc.textFile(s"$baseDir/result/wordSplit/$algorithm/part*", 30)
      .flatMap(data => {
        if(data.split("\t").length < 2) None
        else{
          val sourceKey = data.split("\t")(0)
          val words = data.split("\t")(1).split(" \| ").flatMap(f => {
            val json = JSONObjectEx.fromObject(f.trim)
            if (null != json && "1".equals(json.getStringByKeys("status"))) {
              val jsonArr = try { JSONArray.fromString(json.getStringByKeys("res")) } catch { case _ => null }
              var result: List[(String, String)] = List()
              if (jsonArr != null) {
                for (j <- 0 until jsonArr.length()) {
                  val json = jsonArr.getJSONObject(j)
                  val cont = json.getString("cont")
                  result ::= (cont, cont)
                }
              }
              result.reverse
            } else None
          }).toList
          Some((sourceKey, words))
        }
      }).filter(_._2.nonEmpty)

    splitRDD.cache()

    val totalFilms = splitRDD.count()

    val idfRdd = splitRDD.flatMap(result => {
      result._2.map(_._1).distinct.map((_, 1))
    }).groupByKey().filter(f => f._2.size > 1).map(f => (f._1, Math.log(totalFilms * 1.0 / (f._2.sum + 1))))

    idfRdd.cache()
    idfRdd.map(f => s"${f._1}	${f._2}").saveAsTextFile(HDFSFileUtil.clean(s"$baseDir/result/idf/$algorithm"))

    val idfMap = sc.broadcast(idfRdd.collectAsMap())
    // 计算TF
    val tfRdd = splitRDD.map(result => {
      val totalWords = result._2.size
      val keyWords = result._2.groupBy(_._1)
        .map(f => {
          val word = f._1
          val tf = f._2.size * 1.0 / totalWords
          (tf * idfMap.value.getOrElse(word, 0D), word)
        }).toList.sortBy(_._1).reverse.filter(_._2.trim.length > 1).take(50)
      (result._1, keyWords)
    })

    tfRdd.cache()
    tfRdd.map(f => {
      val json = new JSONObject()
      json.put("_id", f._1)

      val arr = new JSONArray()
      for (keyWord <- f._2) {
        val subJson = new JSONObject()
        subJson.put("score", keyWord._1)
        subJson.put("word", keyWord._2)
        arr.put(subJson)
      }
      json.put("keyWords", arr)
      json.toString
    }).saveAsTextFile(HDFSFileUtil.clean(s"$baseDir/result/keyword/$algorithm/withScore"))

    tfRdd.map(f => s"${f._1}	${f._2.map(_._2).toList.mkString(",")}")
      .saveAsTextFile(HDFSFileUtil.clean(s"$baseDir/result/keyword/$algorithm/noScore"))

    tfRdd.unpersist()

    splitRDD.unpersist()
    idfMap.unpersist()
    idfRdd.unpersist()

    unionInputRdd.unpersist()
    filterWordRdd.unpersist()
    sc.stop()
  }

2) 基于HanLP提供的AC双数组封装

import com.google.common.collect.Lists;
import com.hankcs.hanlp.HanLP;
import com.hankcs.hanlp.seg.Segment;
import com.hankcs.hanlp.seg.common.Term;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.Serializable;
import java.util.List;

public class HanLPSpliter implements Serializable{
    private static Logger logger = LoggerFactory.getLogger(Act.class);

    private static HanLPSpliter instance = null;

    private static Segment segment = null;

    private static final String PATH = "conf/tencent_word_act.txt";

    public static HanLPSpliter getInstance() {
        if(null == instance){
            instance = new HanLPSpliter();
        }
        return instance;
    }

    public HanLPSpliter(){
        this.init();
    }

    public void init(){
        initSegment();
    }

    public void initSegment(){
        if(null == segment){
            addDict();
            HanLP.Config.IOAdapter = new HadoopFileIOAdapter();
            segment = HanLP.newSegment("dat");
            segment.enablePartOfSpeechTagging(true);
            segment.enableCustomDictionaryForcing(true);
        }
    }

    public List<String> seg(String text){
        if(null == segment){
            initSegment();
        }

        List<Term> terms = segment.seg(text);
        List<String> results = Lists.newArrayList();
        for(Term term : terms){
            results.add(term.word);
        }
        return results;
    }
}

3) HanLP加载HDFS中的自定义词典

import com.hankcs.hanlp.corpus.io.IIOAdapter;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;

import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.net.URI;

public class HadoopFileIOAdapter implements IIOAdapter{
    @Override
    public InputStream open(String path) throws IOException {
        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(URI.create(path), conf);
        return fs.open(new Path(path));
    }

    @Override
    public OutputStream create(String path) throws IOException {
        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(URI.create(path), conf);
        OutputStream out = fs.create(new Path(path));
        return out;
    }
}

4. 采坑总结

(1) Spark中实现HanLP自定义词典的加载

由于引入腾讯的嵌入词，因此使用HanLP的自定义词典功能，参考的方法如下：

a. 《基于hanLP的中文分词详解-MapReduce实现&自定义词典文件》，该方法适用于自定义词典的数量较少的情况，如果词典量较大，如腾讯嵌入词820W+，理论上jar包较为臃肿

b. 《Spark中使用HanLP分词》，该方法的好处在于无需手工构件词典的bin文件，操作简单

切记：如果想让自定义词典生效，需先将data/dictionary/custom中的bin文件删除。通过HanLP源码得知，如果存在bin文件，则直接加载该bin文件，否则会将custom中用户自定义的词典重新加载，在指定的环境中(如本地或HDFS)中自动生成bin文件。

腾讯820W词典，基于HanLP生成bin文件的时间大概为30分钟。

(2) Spark异常

Spark执行过程中的异常信息：

1) 异常1

a. 异常信息：

Job aborted due to stage failure: Total size of serialized results of 3979 tasks (1024.2 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)

b. 解决：通过设置spark.driver.maxResultSize=4G，参考：《Spark排错与优化》

2) 异常2

a. 异常信息：java.lang.OutOfMemoryError: Java heap space

b. 解决：参考https://blog.csdn.net/guohecang/article/details/52088117

如有问题，请留言回复！