java 如何使用 SentiWordNet
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How to use SentiWordNet
提问by Belgarion
I need to do sentiment analysis on some csv files containing tweets. I'm using SentiWordNetto do the sentiment analysis.
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 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:\Users\MyName\Desktop\tweets.csv. The path of the SentiWordNet_3.0.0.txtis C:\Users\MyName\Desktop\SentiWordNet_3.0.0\home\swn\www\admin\dump\SentiWordNet_3.0.0_20130122.txt. I'm new to java, pls help, thanks! The link to the sample java code below is this.
我得到了他们在他们的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:\Users\MyName\Desktop\tweets.csv. 的路径SentiWordNet_3.0.0.txt是C:\Users\MyName\Desktop\SentiWordNet_3.0.0\home\swn\www\admin\dump\SentiWordNet_3.0.0_20130122.txt。我是java新手，请帮忙，谢谢！下面的示例 java 代码的链接是这个。
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("\t");
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);
}
}
Newcode:
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("\t");
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
}
回答by Maroun
First of all start by deleting all the "garbage" at the first of the file (which includes description, instruction etc..)
首先删除文件开头的所有“垃圾”（包括描述、说明等）
One possible usage is to change SWN3an make the method extractin it return a Double:
一种可能的用法是更改SWN3使其中的方法extract返回 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;
}
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 extractmethod on each word, you can decide what is the average weight of the String:
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）并使用从extract每个单词的方法返回的结果，您可以决定什么是平均权重字符串：
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";
I found this way easier and it works fine for me.
我发现这种方式更容易，而且对我来说效果很好。
UPDATE:
更新：
I changed _dictto _dict = new HashMap<String, Double>();So it will have a Stringkey and a Doublevalue.
我改_dict到_dict = new HashMap<String, Double>();那么它将有一个String键和一个Double值。
So I replaced _dict.put(word, sent);wish _dict.put(word, score);
所以我替换了_dict.put(word, sent);wish_dict.put(word, score);
回答by user2215524
for that you should write the main function, in that provide the path of csv, extract words from it. and then call extract function by sending the word and its pos.
为此，您应该编写 main 函数，在该函数中提供 csv 的路径，从中提取单词。然后通过发送单词及其位置来调用提取函数。
