Sentiment Analysis(SentiWordNet) - Judging the context of a sentence - nlp

Question

I am trying to find whether a sentence is Positive or Negative in the following steps:
1.) Retrieving the Parts of speech(verbs, nouns, adjectives etc) from the sentence using the Stanford NLP parser.
2.) Using the SentiWordNet to find the Positive and Negative values related to each Part of Speech.
3.) Summing up the Positive and Negative values obtained to calculate a Net Positive and Net Negative value related to a sentence.
But the problem is that, the SentiWordNet return a list of Positive/Negative values based on different senses/contexts. Is it possible to pass a particular sentence along with the part of speech to the SentiWordNet parser, so that it can judge the sense/context automatically and returns only one pair of Positive and Negative value?
Or is there any other alternate solution to this problem?
Thanks.

Answer 1

SentoWordNet Demo Code
This may help you.
// Copyright 2013 Petter Törnberg
//
// This demo code has been kindly provided by Petter Törnberg <pettert#chalmers.se>
// for the SentiWordNet website.
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
public class SentiWordNetDemoCode {
private Map<String, Double> dictionary;
public SentiWordNetDemoCode(String pathToSWN) throws IOException {
// This is our main dictionary representation
dictionary = new HashMap<String, Double>();
// From String to list of doubles.
HashMap<String, HashMap<Integer, Double>> tempDictionary = new HashMap<String, HashMap<Integer, Double>>();
BufferedReader csv = null;
try {
csv = new BufferedReader(new FileReader(pathToSWN));
int lineNumber = 0;
String line;
while ((line = csv.readLine()) != null) {
lineNumber++;
// If it's a comment, skip this line.
if (!line.trim().startsWith("#")) {
// We use tab separation
String[] data = line.split("\t");
String wordTypeMarker = data[0];
// Example line:
// POS ID PosS NegS SynsetTerm#sensenumber Desc
// a 00009618 0.5 0.25 spartan#4 austere#3 ascetical#2
// ascetic#2 practicing great self-denial;...etc
// Is it a valid line? Otherwise, through exception.
if (data.length != 6) {
throw new IllegalArgumentException(
"Incorrect tabulation format in file, line: "
+ lineNumber);
}
// Calculate synset score as score = PosS - NegS
Double synsetScore = Double.parseDouble(data[2])
- Double.parseDouble(data[3]);
// Get all Synset terms
String[] synTermsSplit = data[4].split(" ");
// Go through all terms of current synset.
for (String synTermSplit : synTermsSplit) {
// Get synterm and synterm rank
String[] synTermAndRank = synTermSplit.split("#");
String synTerm = synTermAndRank[0] + "#"
+ wordTypeMarker;
int synTermRank = Integer.parseInt(synTermAndRank[1]);
// What we get here is a map of the type:
// term -> {score of synset#1, score of synset#2...}
// Add map to term if it doesn't have one
if (!tempDictionary.containsKey(synTerm)) {
tempDictionary.put(synTerm,
new HashMap<Integer, Double>());
}
// Add synset link to synterm
tempDictionary.get(synTerm).put(synTermRank,
synsetScore);
}
}
}
// Go through all the terms.
for (Map.Entry<String, HashMap<Integer, Double>> entry : tempDictionary
.entrySet()) {
String word = entry.getKey();
Map<Integer, Double> synSetScoreMap = entry.getValue();
// Calculate weighted average. Weigh the synsets according to
// their 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 (Map.Entry<Integer, Double> setScore : synSetScoreMap
.entrySet()) {
score += setScore.getValue() / (double) setScore.getKey();
sum += 1.0 / (double) setScore.getKey();
}
score /= sum;
dictionary.put(word, score);
}
} catch (Exception e) {
e.printStackTrace();
} finally {
if (csv != null) {
csv.close();
}
}
}
public double extract(String word, String pos) {
return dictionary.get(word + "#" + pos);
}
public static void main(String [] args) throws IOException {
if(args.length<1) {
System.err.println("Usage: java SentiWordNetDemoCode <pathToSentiWordNetFile>");
return;
}
String pathToSWN = args[0];
SentiWordNetDemoCode sentiwordnet = new SentiWordNetDemoCode(pathToSWN);
System.out.println("good#a "+sentiwordnet.extract("good", "a"));
System.out.println("bad#a "+sentiwordnet.extract("bad", "a"));
System.out.println("blue#a "+sentiwordnet.extract("blue", "a"));
System.out.println("blue#n "+sentiwordnet.extract("blue", "n"));
}
}

Answer 2

We can pass the pos to sentiwordnet parser.
Download pattern python module
from pattern.en import wordnet
print wordnet.synsets("kill",pos="VB")[0].weight
wordnet.synsets returns list of synsets
and from that we are selecting 1st item
Output will be a tuple of (polarity,subjectivity)
Hope this helps...