如何使用 SentiWordNet [英] How to use SentiWordNet
问题描述
我需要对一些包含推文的 csv 文件进行情感分析.我正在使用 SentiWordNet 进行情绪分析.
I need to do sentiment analysis on some csv files containing tweets. I'm using SentiWordNet to do the sentiment analysis.
我得到了他们在其网站上提供的以下示例 Java 代码.我不确定如何使用它.我要分析的 csv 文件的路径是 C:UsersMyNameDesktop weets.csv .SentiWordNet_3.0.0.txt的路径为C:UsersMyNameDesktopSentiWordNet_3.0.0homeswnwwwadmindumpSentiWordNet_3.0.0_20130122.txt.我是java新手，请帮忙，谢谢！下面的示例 java 代码的链接是this.
I got the following piece of sample java code they provided on their site. I'm not sure how to use it. The path of the csv file that I want to analyze is C:UsersMyNameDesktop weets.csv . The path of the SentiWordNet_3.0.0.txt is C:UsersMyNameDesktopSentiWordNet_3.0.0homeswnwwwadmindumpSentiWordNet_3.0.0_20130122.txt . I'm new to java, pls help, thanks! The link to the sample java code below is this.
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Set;
import java.util.Vector;
public class SWN3 {
private String pathToSWN = "data"+File.separator+"SentiWordNet_3.0.0.txt";
private HashMap<String, String> _dict;
public SWN3(){
_dict = new HashMap<String, String>();
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(" ");
Double score = Double.parseDouble(data[2])-Double.parseDouble(data[3]);
String[] words = data[4].split(" ");
for(String w:words)
{
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 i = 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 i = 0;i<index; i++)
v.add(0.0);
v.add(index, score);
_temp.put(w_n[0], v);
}
}
}
Set<String> temp = _temp.keySet();
for (Iterator<String> iterator = temp.iterator(); iterator.hasNext();) {
String word = (String) iterator.next();
Vector<Double> v = _temp.get(word);
double score = 0.0;
double sum = 0.0;
for(int i = 0; i < v.size(); i++)
score += ((double)1/(double)(i+1))*v.get(i);
for(int i = 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, sent);
}
}
catch(Exception e){e.printStackTrace();}
}
public String extract(String word, String pos)
{
return _dict.get(word+"#"+pos);
}
}
新代码:
public class SWN3 {
private String pathToSWN = "C:\Users\MyName\Desktop\SentiWordNet_3.0.0\home\swn\www\admin\dump\SentiWordNet_3.0.0.txt";
private HashMap<String, String> _dict;
public SWN3(){
_dict = new HashMap<String, String>();
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(" ");
Double score = Double.parseDouble(data[2])-Double.parseDouble(data[3]);
String[] words = data[4].split(" ");
for(String w:words)
{
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 i = 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 i = 0;i<index; i++)
v.add(0.0);
v.add(index, score);
_temp.put(w_n[0], v);
}
}
}
Set<String> temp = _temp.keySet();
for (Iterator<String> iterator = temp.iterator(); iterator.hasNext();) {
String word = (String) iterator.next();
Vector<Double> v = _temp.get(word);
double score = 0.0;
double sum = 0.0;
for(int i = 0; i < v.size(); i++)
score += ((double)1/(double)(i+1))*v.get(i);
for(int i = 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, sent);
}
}
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 String classifytweet(){
String[] words = twit.split("\s+");
double totalScore = 0, averageScore;
for(String word : words) {
word = word.replaceAll("([^a-zA-Z\s])", "");
if (_sw.extract(word) == null)
continue;
totalScore += _sw.extract(word);
}
Double AverageScore = totalScore;
if(averageScore>=0.75)
return "very positive";
else if(averageScore > 0.25 && averageScore<0.5)
return "positive";
else if(averageScore>=0.5)
return "positive";
else if(averageScore < 0 && averageScore>=-0.25)
return "negative";
else if(averageScore < -0.25 && averageScore>=-0.5)
return "negative";
else if(averageScore<=-0.75)
return "very negative";
return "neutral";
}
public static void main(String[] args) {
// TODO Auto-generated method stub
}
推荐答案
首先删除文件开头的所有垃圾"(包括描述、说明等)
First of all start by deleting all the "garbage" at the first of the file (which includes description, instruction etc..)
一种可能的用法是改变SWN3，使extract方法返回一个Double:
One possible usage is to change SWN3 an make the method extract in it return a Double:
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;
}
然后，给出一个要标记的字符串，您可以将其拆分，使其只有单词(没有符号和未知字符)，并在每个字符串上使用从 extract 方法返回的结果word，你可以决定字符串的平均权重是多少:
Then, giving a String that you want to tag, you can split it so it'll have only words (with no signs and unknown chars) and using the result returned from extract method on each word, you can decide what is the average weight of the String:
String[] words = twit.split("\s+");
double totalScore = 0, averageScore;
for(String word : words) {
word = word.replaceAll("([^a-zA-Z\s])", "");
if (_sw.extract(word) == null)
continue;
totalScore += _sw.extract(word);
}
verageScore = totalScore;
if(averageScore>=0.75)
return "very positive";
else if(averageScore > 0.25 && averageScore<0.5)
return "positive";
else if(averageScore>=0.5)
return "positive";
else if(averageScore < 0 && averageScore>=-0.25)
return "negative";
else if(averageScore < -0.25 && averageScore>=-0.5)
return "negative";
else if(averageScore<=-0.75)
return "very negative";
return "neutral";
我发现这种方法更容易，而且对我来说效果很好.
I found this way easier and it works fine for me.
更新:
我将 _dict 更改为 _dict = new HashMap 所以它将有一个 String 键和一个 <代码>双重值.
I changed _dict to _dict = new HashMap<String, Double>(); So it will have a String key and a Double value.
所以我替换了 _dict.put(word, sent); Wish _dict.put(word, score);
So I replaced _dict.put(word, sent); wish _dict.put(word, score);
这篇关于如何使用 SentiWordNet的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
