/// <summary>
/// Main method to run the crowdsourcing experiments presented in Simpson et.al (WWW15).
/// </summary>
public static void Main()
{
var data = Datum.LoadData(Path.Combine("Data", "weatherTweets.tsv.gz"));
// Run model and get results
var VocabularyOnSubData = ResultsWords.BuildVocabularyOnSubdata((List <Datum>)data);
BCCWords model = new BCCWords();
ResultsWords resultsWords = new ResultsWords(data, VocabularyOnSubData);
DataMappingWords mapping = resultsWords.Mapping as DataMappingWords;
if (mapping != null)
{
resultsWords = new ResultsWords(data, VocabularyOnSubData);
resultsWords.RunBCCWords("BCCwords", data, data, model, Results.RunMode.ClearResults, true);
}
using (var writer = new StreamWriter(Console.OpenStandardOutput()))
{
resultsWords.WriteResults(writer, false, false, false, true);
}
Console.WriteLine("Done. Press enter to exit.");
Console.ReadLine();
}
/// <summary>
/// Writes various results to a StreamWriter.
/// </summary>
/// <param name="writer">A StreamWriter instance.</param>
/// <param name="writeCommunityParameters">Set true to write community parameters.</param>
/// <param name="writeWorkerParameters">Set true to write worker parameters.</param>
/// <param name="writeWorkerCommunities">Set true to write worker communities.</param>
/// <param name="writeProbWords">Set true to write word probabilities</param>
/// <param name="topWords">Number of words to select</param>
public void WriteResults(StreamWriter writer, bool writeCommunityParameters, bool writeWorkerParameters, bool writeWorkerCommunities, bool writeProbWords, int topWords = 30)
{
base.WriteResults(writer, writeCommunityParameters, writeWorkerCommunities, writeWorkerCommunities);
DataMappingWords MappingWords = Mapping as DataMappingWords;
if (writeProbWords && this.ProbWords != null)
{
int NumClasses = ProbWords.Length;
for (int c = 0; c < NumClasses; c++)
{
if (MappingWords != null && MappingWords.WorkerCount > 300) // Assume it's CF
{
writer.WriteLine("Class {0}", MappingWords.CFLabelName[c]);
}
else
if (MappingWords != null)
{
writer.WriteLine("Class {0}", MappingWords.SPLabelName[c]);
}
Vector probs = ProbWords[c].GetMean();
var probsDictionary = probs.Select((value, index) => new KeyValuePair <string, double>(MappingWords.Vocabulary[index], Math.Log(value))).OrderByDescending(x => x.Value).ToArray();
for (int w = 0; w < topWords; w++)
{
writer.WriteLine($"\t{probsDictionary[w].Key}: \t{probsDictionary[w].Value:0.000}");
}
}
}
}
/// <summary>
/// Writes various results to a StreamWriter.
/// </summary>
/// <param name="writer">A StreamWriter instance.</param>
/// <param name="writeCommunityParameters">Set true to write community parameters.</param>
/// <param name="writeWorkerParameters">Set true to write worker parameters.</param>
/// <param name="writeWorkerCommunities">Set true to write worker communities.</param>
/// <param name="writeProbWords">Set true to write word probabilities</param>
/// <param name="topWords">Number of words to select</param>
public void WriteResults(StreamWriter writer, bool writeCommunityParameters, bool writeWorkerParameters, bool writeWorkerCommunities, bool writeProbWords, int topWords = 30)
{
base.WriteResults(writer, writeCommunityParameters, writeWorkerCommunities, writeWorkerCommunities);
DataMappingWords MappingWords = Mapping as DataMappingWords;
if (writeProbWords && this.ProbWords != null)
{
int NumClasses = ProbWords.Length;
var classifiedWords = new Dictionary <string, KeyValuePair <string, double> >();
for (int c = 0; c < NumClasses; c++)
{
string className = string.Empty;
if (MappingWords != null)
{
if (MappingWords.WorkerCount > 100) // Assume it's CF
{
className = MappingWords.CFLabelName[c];
}
else
{
className = MappingWords.SPLabelName[c];
}
writer.WriteLine($"Class {className}");
}
Vector probs = ProbWords[c].GetMean();
var probsDictionary = probs.Select((value, index) => new KeyValuePair <string, double>(MappingWords.Vocabulary[index], Math.Log(value))).OrderByDescending(x => x.Value).ToArray();
topWords = Math.Min(topWords, probsDictionary.Length);
for (int w = 0; w < topWords; w++)
{
writer.WriteLine($"\t{probsDictionary[w].Key}: \t{probsDictionary[w].Value:0.000}");
if (!string.IsNullOrEmpty(className))
{
KeyValuePair <string, double> classifiedWord;
if (!classifiedWords.TryGetValue(probsDictionary[w].Key, out classifiedWord) ||
classifiedWord.Value < probsDictionary[w].Value)
{
classifiedWords[probsDictionary[w].Key] = new KeyValuePair <string, double>(className, probsDictionary[w].Value);
}
}
}
}
writer.WriteLine();
writer.WriteLine($"Main classes:");
foreach (var wordByClass in classifiedWords.GroupBy(classified => classified.Value.Key))
{
writer.WriteLine($"Class {wordByClass.Key}:");
foreach (var word in wordByClass.OrderByDescending(w => w.Value.Value))
{
writer.WriteLine($"\t{word.Key}");
}
}
}
}
/// <summary>
/// Runs the majority vote method on the data.
/// </summary>
/// <param name="modelName"></param>
/// <param name="data">The data</param>
/// <param name="mode"></param>
/// <param name="calculateAccuracy">Compute the accuracy (true).</param>
/// <param name="fullData"></param>
/// <param name="model"></param>
/// <param name="useMajorityVote"></param>
/// <param name="useRandomLabel"></param>
/// <returns>The updated results</returns>
public void RunBCCWords(string modelName,
IList <Datum> data,
IList <Datum> fullData,
BCCWords model,
RunMode mode,
bool calculateAccuracy,
bool useMajorityVote = false,
bool useRandomLabel = false)
{
DataMappingWords MappingWords = null;
if (FullMapping == null)
{
FullMapping = new DataMapping(fullData);
}
if (Mapping == null)
{
// Build vocabulary
Console.Write("Building vocabulary...");
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
string[] corpus = data.Select(d => d.BodyText).Distinct().ToArray();
Vocabulary = BuildVocabularyFromCorpus(corpus);
Console.WriteLine("done. Elapsed time: {0}", stopwatch.Elapsed);
// Build data mapping
this.Mapping = new DataMappingWords(data, MappingWords.Vocabulary);
MappingWords = Mapping as DataMappingWords;
this.GoldLabels = MappingWords.GetGoldLabelsPerTaskId();
}
MappingWords = Mapping as DataMappingWords;
int[] trueLabels = null;
if (useMajorityVote)
{
if (MappingWords != null)
{
var majorityLabel = MappingWords.GetMajorityVotesPerTaskId(data);
trueLabels = Util.ArrayInit(FullMapping.TaskCount, i => majorityLabel.ContainsKey(Mapping.TaskIndexToId[i]) ? (int)majorityLabel[Mapping.TaskIndexToId[i]] : Rand.Int(Mapping.LabelMin, Mapping.LabelMax + 1));
data = MappingWords.BuildDataFromAssignedLabels(majorityLabel, data);
}
}
if (useRandomLabel)
{
var randomLabels = MappingWords.GetRandomLabelPerTaskId(data);
data = MappingWords.BuildDataFromAssignedLabels(randomLabels, data);
}
var labelsPerWorkerIndex = MappingWords.GetLabelsPerWorkerIndex(data);
var taskIndicesPerWorkerIndex = MappingWords.GetTaskIndicesPerWorkerIndex(data);
// Create model
ClearResults();
model.CreateModel(MappingWords.TaskCount, MappingWords.LabelCount, MappingWords.WordCount);
// Run model inference
BCCWordsPosteriors posteriors = model.InferPosteriors(labelsPerWorkerIndex, taskIndicesPerWorkerIndex, MappingWords.WordIndicesPerTaskIndex, MappingWords.WordCountsPerTaskIndex, trueLabels);
// Update results
UpdateResults(posteriors, mode);
// Compute accuracy
if (calculateAccuracy)
{
UpdateAccuracy();
}
}
/// <summary>
/// Writes various results to a StreamWriter.
/// </summary>
/// <param name="writer">A StreamWriter instance.</param>
/// <param name="writeCommunityParameters">Set true to write community parameters.</param>
/// <param name="writeWorkerParameters">Set true to write worker parameters.</param>
/// <param name="writeWorkerCommunities">Set true to write worker communities.</param>
/// <param name="writeProbWords">Set true to write word probabilities</param>
/// <param name="topWords">Number of words to select</param>
public void WriteResults(StreamWriter writer, bool writeCommunityParameters, bool writeWorkerParameters, bool writeWorkerCommunities, bool writeProbWords, int topWords = 30)
{
base.WriteResults(writer, writeCommunityParameters, writeWorkerCommunities, writeWorkerCommunities);
DataMappingWords MappingWords = Mapping as DataMappingWords;
if (writeProbWords && this.ProbWords != null)
{
int NumClasses = ProbWords.Length;
var classifiedWords = new Dictionary <string, KeyValuePair <string, double> >();
for (int c = 0; c < NumClasses; c++)
{
string className = string.Empty;
if (MappingWords != null)
{
if (MappingWords.WorkerCount > 100) // Assume it's CF
{
className = MappingWords.CFLabelName[c];
}
else
{
className = MappingWords.SPLabelName[c];
}
writer.WriteLine($"Class {className}");
}
Vector probs = ProbWords[c].GetMean();
var probsDictionary = probs.Select((value, index) => new KeyValuePair <string, double>(MappingWords.Vocabulary[index], Math.Log(value))).OrderByDescending(x => x.Value).ToArray();
topWords = Math.Min(topWords, probsDictionary.Length);
for (int w = 0; w < topWords; w++)
{
writer.WriteLine($"\t{probsDictionary[w].Key}: \t{probsDictionary[w].Value:0.000}");
if (!string.IsNullOrEmpty(className))
{
KeyValuePair <string, double> classifiedWord;
if (!classifiedWords.TryGetValue(probsDictionary[w].Key, out classifiedWord) ||
classifiedWord.Value < probsDictionary[w].Value)
{
classifiedWords[probsDictionary[w].Key] = new KeyValuePair <string, double>(className, probsDictionary[w].Value);
}
}
}
}
writer.WriteLine();
//Create wordCloud
var wordCloudPositif = new WordCloud.WordCloud(500, 500, true);
var wordCloudNegatif = new WordCloud.WordCloud(500, 500, true);
var wordCloudNeutral = new WordCloud.WordCloud(500, 500, true);
var wordCloudUnknown = new WordCloud.WordCloud(500, 500, true);
var wordCloud = new WordCloud.WordCloud(500, 500, true);
//Create ListWords
List <String> wordsPositif = new List <string> {
};
List <String> wordsNegatif = new List <string> {
};
List <String> wordsNeutral = new List <string> {
};
List <String> wordsUnknown = new List <string> {
};
List <String> words = new List <string> {
};
//Create ListFrequencies
List <int> frequenciesPositif = new List <int> {
};
List <int> frequenciesNegatif = new List <int> {
};
List <int> frequenciesNeutral = new List <int> {
};
List <int> frequenciesUnknown = new List <int> {
};
List <int> frequencies = new List <int> {
};
//Create Bitmap
Bitmap myBitmapPositif = null;
Bitmap myBitmapNegatif = null;
Bitmap myBitmapNeutral = null;
Bitmap myBitmapUnknown = null;
Bitmap myBitmap = null;
writer.WriteLine($"Main classes:");
foreach (var wordByClass in classifiedWords.GroupBy(classified => classified.Value.Key))
{
writer.WriteLine($"Class {wordByClass.Key}:");
foreach (var word in wordByClass.OrderByDescending(w => w.Value.Value))
{
writer.WriteLine($"\t{word.Key}");
if (wordByClass.Key == "Negative")
{
wordsNegatif.Add($"{word.Key}");
frequenciesNegatif.Add(1);
}
else if (wordByClass.Key == "Neutral")
{
wordsNeutral.Add($"{word.Key}");
frequenciesNeutral.Add(1);
}
else if (wordByClass.Key == "Positive")
{
wordsPositif.Add($"{word.Key}");
frequenciesPositif.Add(1);
}
else if (wordByClass.Key == "NotRelated")
{
words.Add($"{word.Key}");
frequencies.Add(1);
}
else if (wordByClass.Key == "Unknown")
{
wordsUnknown.Add($"{word.Key}");
frequenciesUnknown.Add(1);
}
}
}
//Creation wordCloud Positif
myBitmapPositif = new Bitmap(wordCloudPositif.Draw(wordsPositif, frequenciesPositif));
myBitmapPositif.Save("C:/Users/athen/OneDrive/Documents/EPF/5A/IA/Sentimental-Analysis/WordClouds/Positive.jpg");
//Creation wordCloud Negatif
myBitmapNegatif = new Bitmap(wordCloudNegatif.Draw(wordsNegatif, frequenciesNegatif));
myBitmapNegatif.Save("C:/Users/athen/OneDrive/Documents/EPF/5A/IA/Sentimental-Analysis/WordClouds/Negative.jpg");
//Creation wordCloud Neutral
myBitmapNeutral = new Bitmap(wordCloudNeutral.Draw(wordsNeutral, frequenciesNeutral));
myBitmapNeutral.Save("C:/Users/athen/OneDrive/Documents/EPF/5A/IA/Sentimental-Analysis/WordClouds/Neutral.jpg");
//Creation wordCloud Unkown
myBitmapUnknown = new Bitmap(wordCloudUnknown.Draw(wordsUnknown, frequenciesUnknown));
myBitmapUnknown.Save("C:/Users/athen/OneDrive/Documents/EPF/5A/IA/Sentimental-Analysis/WordClouds/Unknown.jpg");
//Creation wordCloud NotRelated
myBitmap = new Bitmap(wordCloud.Draw(words, frequencies));
myBitmap.Save("C:/Users/athen/OneDrive/Documents/EPF/5A/IA/Sentimental-Analysis/WordClouds/notRelated.jpg");
}
}
