static void Main(string[] args)
{
Console.WriteLine("[START]\tTraining started");
NaiveBayesClassifier sentimentClassifier = new NaiveBayesClassifier();
sentimentClassifier.Train(new TestDataParser("Resources/SentimentTrainingData.txt"));
Console.WriteLine("[END]\tTraining");
Console.WriteLine();
Console.WriteLine("[START]\tFriendlist parsing");
FriendListParser parser = new FriendListParser("Resources/friendships.reviews.txt");
parser.Parse();
File.WriteAllText("graph.csv", parser.Community.ToGraph());
Console.WriteLine("[END]\t\tFriendlist parsing");
Console.WriteLine();
Console.WriteLine("[START]\t\tIdentifying communities");
CommunityIdentifier communityIdentifier = new CommunityIdentifier(parser.Community);
DateTime start = DateTime.Now;
List<Community> subCommunities = communityIdentifier.Identify();
Console.WriteLine("[END]\t\tIdentifying communities");
Console.WriteLine();
Console.WriteLine("[START]\t\tAnalysing communities");
CommunityAnalyzer communityAnalyzer = new CommunityAnalyzer(sentimentClassifier, subCommunities);
string filename = "result.csv";
communityAnalyzer.Analyze(filename);
Console.WriteLine("[END]\t\tAnalysing communities: Results written to {0}", filename);
Console.WriteLine("Identied communities: " + subCommunities.Count);
Console.WriteLine("\nSub communities:");
foreach (var community in subCommunities)
{
Console.WriteLine(community.Size);
community.Dispose();
}
communityAnalyzer.Dispose();
communityIdentifier.Dispose();
sentimentClassifier.Dispose();
Console.WriteLine(DateTime.Now.Subtract(start).ToString("g"));
Console.WriteLine("Finished everything. Press to exit");
Console.ReadLine();
}
public static void Main()
{
double averageLog = 0;
double average = 0;
Console.WriteLine("Started parsing");
TestDataParser parser = new TestDataParser("Resources/SentimentTrainingData.txt");
parser.Parse();
Console.WriteLine("Finished parsing\n");
List<Review> posReviews = new List<Review>();
List<Review> negReviews = new List<Review>();
Console.WriteLine("Sorting reviews");
foreach (var review in parser.Reviews)
{
if (review.Rating == Label.Neg)
negReviews.Add(review);
else if (review.Rating == Label.Pos)
posReviews.Add(review);
}
List<Review>[] folds = SplitReviews(posReviews, 10);
List<Review>[] negFold = SplitReviews(negReviews, 10);
Console.WriteLine("Combing reviews");
for (int i = 0; i < folds.Length; i++)
{
folds[i].AddRange(negFold[i]);
}
Console.WriteLine("Testing reviews:\n");
for (int j = 0; j < folds.Length; j++)
{
List<Review> testReviews = folds[j];
List<Review> trainReviews = new List<Review>();
NaiveBayesClassifier classifier = new NaiveBayesClassifier();
for (int i = 0; i < folds.Length; i++)
{
if (j == i) continue;
trainReviews.AddRange(folds[i]);
}
classifier.Train(trainReviews);
int correctLog = 0;
int correct = 0;
foreach (var testReview in testReviews)
{
double posScoreLog = classifier.ScoreLog(testReview.Summary + testReview.Text, Label.Pos);
double negScoreLog = classifier.ScoreLog(testReview.Summary + testReview.Text, Label.Neg);
Label calculatedLabel;
if (posScoreLog > negScoreLog)
calculatedLabel = Label.Pos;
else
calculatedLabel = Label.Neg;
if (calculatedLabel == testReview.Rating)
correctLog++;
double posScore = classifier.Score(testReview.Summary + testReview.Text, Label.Pos);
double negScore = classifier.Score(testReview.Summary + testReview.Text, Label.Neg);
if (posScore > negScore)
calculatedLabel = Label.Pos;
else
calculatedLabel = Label.Neg;
if (calculatedLabel == testReview.Rating)
correct++;
}
averageLog += ((double)correctLog)/testReviews.Count;
Console.WriteLine("[LogS]\tTest fold {0}: {1} / {2} = {3}", j, correctLog, testReviews.Count, ((double)correctLog)/testReviews.Count);
Console.WriteLine("[S]\tTest fold {0}: {1} / {2} = {3}", j, correct, testReviews.Count, ((double)correct) / testReviews.Count);
}
Console.WriteLine("[LogS]\tAverage: " + averageLog * 100);
Console.WriteLine("[S]\tAverage: " + average * 100);
Console.ReadLine();
}
public CommunityAnalyzer(NaiveBayesClassifier sentimentClassifier, List<Community> communities)
{
_communities = communities;
_sentimentClassifier = sentimentClassifier;
}
