liblinear文本分类开发

训练模型

读取语料和词典进行训练

Scanner getCorpus = new Scanner(new BufferedInputStream(new FileInputStream(new File(corpus))), "UTF-8"); //语料
        Scanner getDict = new Scanner(new BufferedInputStream(new FileInputStream(new File(dict))), "UTF-8");   //词典
//pair对即文本属于消极倾向<-1,"文本">
ListString>> lines = new LinkedListString>>();
Map<String, Integer> features = new LinkedHashMap<String, Integer>();
//语料处理
while(getCorpus.hasNextLine()){
    String line = getCorpus.nextLine();
    String[] _ = line.trim().split("\t");
    lines.add(new PairString>(_[0].equals("负面")? -1: 1, _[1]));  //这里可以根据需要进行多分类   
}
//词典处理
for(int i=0; getDict.hasNextLine(); ){
       String line = getDict.nextLine();
       features.put(line.trim(), ++i);
}
int[] cnt = new int[features.size()];
Arrays.fill(cnt, 0);

for(PairString> pr: lines){
            int polarity = pr.getFirst();
            String phrase = pr.getSecond();

            double sum = 0;
            List> featurePos = StringUtil.featureExtract(phrase, features, 10, 1);
            Set num = new TreeSet();
            for(Pair pos : featurePos){
                String feature = phrase.substring(pos.getFirst(), pos.getSecond());
                int id = features.get(feature);
                sum += cnt[id];
                cnt[id]++;
                num.add(id);
            }
            //文件输出
            if(!featurePos.isEmpty()) {
                putProblem.printf("%d", polarity);
                for(Integer id: num){
                    putProblem.printf(" %d:%.6f", id, cnt[id]/Math.sqrt(sum));
                    cnt[id]=0;
                }
                putProblem.println();
            }
        }
       //使用lR进行训练
      Parameter param = new Parameter(SolverType.L2R_LR, 10, 0.002);
      Linear.train(Train.readProblem(new File(problem), -1), param).save(new File(model)); //训练结束获得model文件

加载特征词

Map<String,Integer> featureMap = new HashMap<String, Integer>();
InputStream inputStream = conf.getConfResourceAsInputStream("path://");
BufferedReader br = new BufferedReader(new InputStreamReader(inputStream));
// Load Feature
String line;
int index = 0;
while ((line = br.readLine()) != null) {
    line = line.toLowerCase().trim();
    if(!featureMap.containsKey(line)){
        featureMap.put(line, ++index);
    }
}
br.close();
inputStream.close();

获取特征向量

List kws;
private int[] cnt = null;
Arrays.fill(cnt, 0);
ArrayList features = new ArrayList();
//通过算法获取关键词（正向匹配、反向匹配等算法）
 kws = StrAlg.Content(content);
 int sum = 0;
 Set num = new TreeSet();
 for (String kw : kws) 
     {
     if (featureMap.containsKey(kw)) {
                int id = featureMap.get(kw);
                sum += cnt[id];
                cnt[id]++;
                num.add(id);
    }
}
for(Integer id : num){
   features.add(new FeatureNode(id, cnt[id]/Math.sqrt(sum)));
   cnt[id]=0;
}

依据训练出的模型进行预测分类

 double threshold = 0.5;  //判别域（属于哪个分类）
 Feature[] instance = new Feature[features.size()];
 features.toArray(instance);
 double[] prob = new double[2];
 double rst = Linear.predictProbability(model, instance, prob);

 if (Math.abs(prob[0]- prob[1]) < 0.1) {
     return 0;
 }
 if (prob[0] > threshold ) {
     return prob[0];
 } else if (prob[1] > threshold ) {
     return prob[1] * (-1);
 }