/// <summary>
/// 清洗训练样本,返回d和total
/// </summary>
/// <param name="negFilePath">负面词训练集</param>
/// <param name="negWords">负面词库文本</param>
/// <param name="posFilePath">正面词训练集</param>
/// <param name="posWords">正面词库文本</param>
/// <param name="d">存储正面和负面词集的字典</param>
/// <param name="stopwords">排除词</param>
public static void Train_data(string negWords, string negFilters, string posWords, string posFilters,
ref Dictionary <string, AddOneProb> d, string stopwords)
{
List <Tuple <List <string>, string> > data = new List <Tuple <List <string>, string> >();
var sent_cut = new Jieba();
sent_cut.NegWords = negFilters;
sent_cut.PosWords = posFilters;
foreach (var sent in posWords.Replace("\r", "").Split('\n'))
{
if (string.IsNullOrWhiteSpace(sent))
{
continue;
}
sent_cut.doc = FilterSymbol(sent);
sent_cut.stopwords = stopwords;
var words = sent_cut.handle_sentiment(false);
foreach (var word in words)
{
d["pos"].Add(word, CoreCalculations.IDCount, 1);
}
if (words != null && words.Count > 0)
{
data.Add(new Tuple <List <string>, string>(words, "pos"));
}
}
Console.WriteLine("正面词库导入完毕");
foreach (var sent in negWords.Replace("\r", "").Split('\n'))
{
if (string.IsNullOrWhiteSpace(sent))
{
continue;
}
sent_cut.doc = FilterSymbol(sent);
sent_cut.stopwords = stopwords;
var words = sent_cut.handle_sentiment();
foreach (var word in words)
{
d["neg"].Add(word, CoreCalculations.IDCount, 1);
}
if (words != null && words.Count > 0)
{
data.Add(new Tuple <List <string>, string>(words, "neg"));
}
}
Console.WriteLine("负面词库导入完毕");
foreach (var k in d)
{//计算频率
k.Value.CalculatRate();
}
for (int i = 0; i < 2; i++)
{
foreach (var d_ in data)
{
var c = d_.Item2.ToString();
CoreCalculations.Sensor(d, d_.Item1, c);//每一条数据做训练
}
}
}
/// <summary>
/// 执行分类
/// </summary>
/// <param name="sent"></param>
/// <param name="d"></param>
/// <param name="total"></param>
/// <param name="stopwords"></param>
/// <returns></returns>
public static Dictionary <string, double> Classify_(string sent, Dictionary <string, AddOneProb> d, string stopwords)
{
Jieba jiebaword = new Jieba();
jiebaword.doc = Train.FilterSymbol(sent);
jiebaword.stopwords = stopwords;
return(Sensor(d, jiebaword.JiebaCut()));
}
