Sentimental Analysis Class — read text files within the jar file.

MapReduceSentimentScore.java

import java.io.*;
import java.util.Hashtable;

/**
 * Created with IntelliJ IDEA.
 * User: joonhyunglim
 * Date: 5/18/13
 * Time: 8:31 PM
 * To change this template use File | Settings | File Templates.
 */
public class MapReduceSentimentScore {

/*
   OOP class for the sentimental analysis.
   It takes a sentence and spit out a value (integer number) corresponding to the sentence's sentiment.
 */

    Hashtable<String,Integer> hashtable;
    Hashtable<String,String> htstem;
    String posline, negline, nounline = null;
    String verbline, adjline, advline = null;
    int posnum = 0, negnum = 0, nounnum=0, verbnum=0,adjnum=0,advnum=0;

    public MapReduceSentimentScore()  {

    try{
        InputStream is = this.getClass().getResourceAsStream("positive-words.txt");
        // get ready for the sentiment analysis; prepare stem dictionaries, positive negative words dictionary.
//        File pos = new File();
        InputStream  neg = this.getClass().getResourceAsStream("negative-words.txt");
        InputStream noun = this.getClass().getResourceAsStream("noun.txt");
        InputStream  verb = this.getClass().getResourceAsStream("verb.txt");
        InputStream  adv = this.getClass().getResourceAsStream("adv.txt");
        InputStream adj = this.getClass().getResourceAsStream("adj.txt");


        BufferedInputStream posi = new BufferedInputStream(is);
        BufferedReader posin = new BufferedReader(new InputStreamReader(new DataInputStream(posi)));
        BufferedReader negin = new BufferedReader(new InputStreamReader(new DataInputStream(neg)));
        BufferedReader nounin = new BufferedReader(new InputStreamReader(new DataInputStream(noun)));
        BufferedReader verbin = new BufferedReader(new InputStreamReader(new DataInputStream(verb)));
        BufferedReader adjin = new BufferedReader(new InputStreamReader(new DataInputStream(adv)));
        BufferedReader advin = new BufferedReader(new InputStreamReader(new DataInputStream(adj)));


        hashtable = new Hashtable<String,Integer>();
        htstem = new Hashtable<String,String>();

        // loading those data into hash tables
        while ((posline = posin.readLine()) != null) {
            hashtable.put(posline, 1);
            posnum++;
        }

        while ((negline = negin.readLine()) != null) {
            hashtable.put(negline, -1);
            negnum++;
        }
        while ((nounline = nounin.readLine()) != null) {
            String tokens[] =  nounline.split(" ");
            htstem.put(tokens[0], tokens[1]);
            nounnum++;
        }

        while ((verbline = verbin.readLine()) != null) {
            String tokens[] =  verbline.split(" ");
            htstem.put(tokens[0], tokens[1]);
            verbnum++;
        }
        while ((adjline = adjin.readLine()) != null) {
                String tokens[] =  adjline.split(" ");
                 htstem.put(tokens[0], tokens[1]);
                adjnum++;

        }while ((advline = advin.readLine()) != null) {
                String tokens[] =  advline.split(" ");
                htstem.put(tokens[0], tokens[1]);
                advnum++;
        }

        // this code below will print out whether it is ready for the analysis.
        System.out.println(posnum+" of positive words and "+negnum+" of negative words, "+nounnum+" nouns, "+verbnum+" verbs, "+adjnum+" adjs, "+advnum+" advs are ready for use.");

       // close the input files
        posin.close();
        negin.close();
        nounin.close();
        verbin.close();
        adjin.close();
        advin.close();

    } catch (IOException i) {
             i.printStackTrace();
        }
    }


    public Integer analysis(String sentence) {
        // this bracket below is the part for actual analysis.
            String line = sentence;
            String[] tweets;
            int rownum = 0;
            int pcatchnum = 0;
            int ncatchnum = 0;


            if (!line.isEmpty()) {
                int value = 0;
                tweets = line.split(" ");
                for(String i : tweets) {
                    // ignore all none word characters
                    i = i.replaceAll("\\W", "");

                    /* if we can find it from pos & neg words, then use it.
                       otherwise, try stemming and do it again.
                     */
                    if (hashtable.get(i)!=null){
                        value += hashtable.get(i);
                    } else { // if we cannot, try to stem it and find it again.

                        String stemmed;
                        if(htstem.get(i)!=null){
                            stemmed = htstem.get(i);
                            // this is not necessary but will give you an idea of which words are being caught by stemming.
//                           System.out.println("****  "+i+" is stemmed");

                        } else {
                            stemmed = i;
                        }

                        if (hashtable.get(stemmed)!=null){
                            value += hashtable.get(stemmed);
                        }
                    }
                }

                if (value > 0) {
                    return 1; // this implies positive.
                } else if (value == 0) {
                    return 2; // this implies neutral.
                } else {
                    return 3; // this implies negative.
                }

            }

        return null; // this implies error!
    }

//}
 // we can check if this sentiment analysis class actually does its job :)
    public static void main(String[] args) {
        int value = 0;
        String[] sentences = new String[] {"Benedict is a good student?", "Do you know who Benedict is?", "Joon is a reliable person", "is Joon a bad student?", "Samsung is bad phone.", "fjwefjpoewjfopewjfopewjfopewjofjwep"};
        MapReduceSentimentScore good = new MapReduceSentimentScore();

        for (int i = 0; i < sentences.length; i++) {
           value =  good.analysis(sentences[i]);
            System.out.println(sentences[i]+" : "+value);
        }

    }
}

The jar file corresponding to this class is in my Github.
It's called "MRSentiment.jar"

Cheers,
Joon