senti wordnet - How to predict sentiment using sentiwordnet -

i need sentiment analysis on csv files containing movie reviews. i'm using sentiwordnet sentiment analysis.the main problem facing in main() function .here can find polarity of single sentence.(string sentence="i love hate current political climate.")but want use complete csv file , find polarity of each review.the path sentiwordnet "c:\users\intel\desktop\sentiment final\finalsentiment.txt"

package swn3; import java.io.bufferedreader; import java.io.file; import java.io.fileinputstream; import java.io.filereader; import java.io.inputstreamreader; import java.util.arrays; import java.util.hashmap; import java.util.iterator; import java.util.scanner; import java.util.set; import java.util.vector;  public class swn3 {     private string pathtoswn = "c:\\users\\intel\\desktop\\sentiment final\\finalsentiment.txt";         private hashmap<string, double> _dict;           public swn3(){              _dict = new hashmap<string, double>();             hashmap<string, vector<double>> _temp = new hashmap<string, vector<double>>();             try{                 bufferedreader csv =  new bufferedreader(new filereader(pathtoswn));                 string line = "";                            while((line = csv.readline()) != null)                 {                     string[] data = line.split("\t");                     double score = double.parsedouble(data[2])-double.parsedouble(data[3]);// calculate synset score score = poss - negs                     string[] words = data[4].split(" ");// synset terms                     for(string w:words)//go through terms of current synset.                     { // synterm , synterm rank                         string[] w_n = w.split("#");                         w_n[0] += "#"+data[0];                         int index = integer.parseint(w_n[1])-1;                         if(_temp.containskey(w_n[0]))                         {                             vector<double> v = _temp.get(w_n[0]);                             if(index>v.size())                                 for(int = v.size();i<index; i++)                                     v.add(0.0);                             v.add(index, score);                             _temp.put(w_n[0], v);                         }                         else                         {                             vector<double> v = new vector<double>();                             for(int = 0;i<index; i++)                                 v.add(0.0);                             v.add(index, score);                             _temp.put(w_n[0], v);                         }                     }                 }                 set<string> temp = _temp.keyset();                 (iterator<string> iterator = temp.iterator(); iterator.hasnext();) {                     string word = (string) iterator.next();                     vector<double> v = _temp.get(word);                     // calculate weighted average. weigh synsets according rank.                     // score= 1/2*first + 1/3*second + 1/4*third ..... etc.                     // sum = 1/1 + 1/2 + 1/3 ...                     double score = 0.0;                     double sum = 0.0;                     for(int = 0; < v.size(); i++)                         score += ((double)1/(double)(i+1))*v.get(i);                     for(int = 1; i<=v.size(); i++)                         sum += (double)1/(double)i;                     score /= sum;                     string sent = "";                                    if(score>=0.75)                         sent = "strong_positive";                     else                     if(score > 0.25 && score<=0.5)                         sent = "positive";                     else                     if(score > 0 && score<=0.25)                         sent = "weak_positive";                     else                     if(score < 0 && score>=-0.25)                         sent = "weak_negative";                     else                     if(score < -0.25 && score>=-0.5)                         sent = "negative";                     else                     if(score<=-0.75)                         sent = "strong_negative";                     _dict.put(word, score);                 }             }             catch(exception e){e.printstacktrace();}                 }  public double extract(string word) {     double total = new double(0);     if(_dict.get(word+"#n") != null)          total = _dict.get(word+"#n") + total;     if(_dict.get(word+"#a") != null)         total = _dict.get(word+"#a") + total;     if(_dict.get(word+"#r") != null)         total = _dict.get(word+"#r") + total;     if(_dict.get(word+"#v") != null)         total = _dict.get(word+"#v") + total;     return total; } public static void main(string[] args) throws exception {     swn3 test = new swn3();     string sentence="i love hate current political climate.";     string[] words = sentence.split("\\s+"); //splits sentence , put array.     double totalscore = 0;     int review=0;     for(string word : words) {         word = word.replaceall("([^a-za-z\\s])", "");//^ means not , a-za-z means upper , lower case characters.overall means not letters         if (test.extract(word) == null)             continue;         totalscore += test.extract(word);      }                     if(totalscore>=0.75)                         review= 5;                     else                     if(totalscore> 0.25 && totalscore<=0.5)                         review = 4;                     else                     if(totalscore > 0 && totalscore<=0.25)                         review = 3;                     else                     if(totalscore< 0 && totalscore>=-0.25)                         review= 2;                     else                     if(totalscore< -0.25 && totalscore>=-0.5)                         review= 1;                     else                     if(totalscore<=-0.75)                         review= 0;    system.out.println(review);     system.out.println(totalscore);   }  }