public void Learn(Sentence sentence)
{
if(sentence.Sentiment == Sentiment.Positive)
{
positiveSentances++;
allCount++;
for (int i = 0; i < context.SentimentWords.Count; i++)
{
var w = context.SentimentWords[i];
if (!positiveDict.ContainsKey(w))
positiveDict.Add(w, new int[2]);
if (sentence.SentimentWords.Contains(i))
positiveDict[w][1]++;
else
positiveDict[w][0]++;
}
}
else
{
negtiveSentances++;
allCount++;
for (int i = 0; i < context.SentimentWords.Count; i++)
{
var w = context.SentimentWords[i];
if (!negativeDict.ContainsKey(w))
negativeDict.Add(w, new int[2]);
if (sentence.SentimentWords.Contains(i))
negativeDict[w][1]++;
else
negativeDict[w][0]++;
}
}
}
public void Analize(Sentence sentence)
{
var distpos = GetDist(sentence, positiveDict, positiveSentances);
var distneg = GetDist(sentence, negativeDict, negtiveSentances);
if (distpos > distneg)
sentence.Sentiment = Sentiment.Positive;
else
sentence.Sentiment = Sentiment.Negative;
}
static void Print(Sentence s, AnalysisContext context)
{
Console.WriteLine(s.Text);
Console.WriteLine("Entities:");
foreach(var i in s.Entities)
{
var word = context.Entities[i];
Console.WriteLine("-->"+word.ToString());
}
Console.WriteLine("Sentiment:" + s.Sentiment);
Console.WriteLine();
}
static void Main(string[] args)
{
//Stemmer st = new Stemmer();
//while (true)
{
// var res = st.Stem(Console.ReadLine());
// Console.WriteLine(res);
}
//var matrix = WordsMatrix.Build("Files\\input_learn_pos.txt");
var cont = AnalysisContext.CreateContext();
BaesianClassifier baes = new BaesianClassifier(cont);
baes.Teach();
/*foreach (var line in File.ReadAllLines("Files\\input_learn_pos.txt"))
{
Sentence s = new Sentence(cont, line);
s.GetWords();
s.Sentiment = Sentiment.Positive;
baes.Learn(s);
}
foreach (var line in File.ReadAllLines("Files\\input_learn_neg.txt"))
{
Sentence s = new Sentence(cont, line);
s.GetWords();
s.Sentiment = Sentiment.Negative;
baes.Learn(s);
}
*/
List<Tuple<IWord, Sentiment>> list = new List<Tuple<IWord, Sentiment>>();
foreach (var line in File.ReadAllLines("Files\\input.txt"))
{
Sentence s = new Sentence(cont, line);
s.GetWords();
baes.Analize(s);
Print(s, cont);
foreach (var i in s.Entities)
{
var word = cont.Entities[i];
list.Add(new Tuple<IWord, Sentiment>(word, s.Sentiment));
}
}
Console.WriteLine("Результат анализа");
foreach (var t in list)
Console.WriteLine(t.Item1 + " -> " + t.Item2);
Console.ReadLine();
}
public static int Analize([MarshalAs(UnmanagedType.LPWStr)]string text)
{
var sentance = new Sentence(Classifier.Context, text);
Classifier.Analize(sentance);
return (int)sentance.Sentiment;
}
public void Teach()
{
foreach (var line in File.ReadAllLines(Constants.LearnPositiveFile))
{
Sentence s = new Sentence(context, line);
s.GetWords();
s.Sentiment = Sentiment.Positive;
Learn(s);
}
foreach (var line in File.ReadAllLines(Constants.LearnNegativeFile))
{
Sentence s = new Sentence(context, line);
s.GetWords();
s.Sentiment = Sentiment.Negative;
Learn(s);
}
}
double GetDist(Sentence sentence, Dictionary<IWord,int[]> dict, int classcount)
{
double dist = classcount / (allCount * 1.0);
for (int i = 0; i < context.SentimentWords.Count; i++)
{
var word = context.SentimentWords[i];
if (sentence.SentimentWords.Contains(i))
dist *= dict[word][1] / (classcount * 1.0);
else
dist *= dict[word][0] / (classcount * 1.0);
}
return dist;
}
