protected override async Task ExecuteAsync(CancellationToken stoppingToken)
{
Console.WriteLine($"Building sentiment classifier...");
var sw = new Stopwatch();
sw.Start();
SentimentClassifierBuilder analyser = new SentimentClassifierBuilder();
var engine = await analyser.BuildEngine();
sw.Stop();
Console.WriteLine($"Finished in {(int)sw.Elapsed.TotalSeconds}s");
string[] searches = new string[]
{
"microsoft",
"apple",
"google",
"amazon",
"facebook",
"ibm",
"dell",
"sony",
"panasonic",
"intel",
"hp"
};
while (!stoppingToken.IsCancellationRequested)
{
foreach (var line in searches)
{
HashSet <string> allTweets = new HashSet <string>();
ConcurrentDictionary <DateTime, DailySentiment> dailySentiments = new ConcurrentDictionary <DateTime, DailySentiment>();
var dates = new DateTime[]
{
DateTime.Today.AddDays(-6),
DateTime.Today.AddDays(-5),
DateTime.Today.AddDays(-4),
DateTime.Today.AddDays(-3),
DateTime.Today.AddDays(-2),
DateTime.Today.AddDays(-1),
DateTime.Today,
};
foreach (var date in dates)
{
if (File.Exists(GetFileName(line, date.AddDays(-1))))
{
continue;
}
await foreach (var statuses in _twitterSearch.Search(line, date.AddDays(-1), date))
{
foreach (var status in statuses)
{
if (!allTweets.Add(status.Text))
{
continue;
}
var result = engine.Predict(status.Text);
var dailySentiment = dailySentiments.GetOrAdd(status.Created_At.Date, new DailySentiment {
SearchTerm = line, Date = status.Created_At.Date
});
if (result.sentiment == Sentiment.Good)
{
dailySentiment.PositiveCount++;
}
else
{
dailySentiment.NegativeCount++;
}
}
}
foreach (var dailySentiment in dailySentiments)
{
string text = JsonConvert.SerializeObject(dailySentiment.Value);
string fileName = GetFileName(line, dailySentiment.Key);
File.WriteAllText(fileName, text);
Console.WriteLine($"Saved {fileName}");
}
}
string GetFileName(string search, DateTime date)
{
return($"data/{search}_{date:yyyyMMdd}.json");
}
}
}
}
protected override async Task ExecuteAsync(CancellationToken stoppingToken)
{
Console.WriteLine($"Building sentiment classifier...");
var sw = new Stopwatch();
sw.Start();
SentimentClassifierBuilder analyser = new SentimentClassifierBuilder();
var engine = await analyser.BuildEngine();
sw.Stop();
Console.WriteLine($"Finished in {(int)sw.Elapsed.TotalSeconds}s");
while (!stoppingToken.IsCancellationRequested)
{
Console.WriteLine($"Please enter search term.");
string line = Console.ReadLine();
int bad = 0;
int good = 0;
int maxSamples = 3;
List <string> badSamples = new List <string>();
List <string> goodSamples = new List <string>();
await foreach (var statuses in _twitterSearch.Search(line, DateTime.UtcNow.AddDays(-1)))
{
foreach (var status in statuses)
{
var result = engine.Predict(status.Text);
if (result.sentiment == Sentiment.Good)
{
good++;
if (goodSamples.Count < maxSamples)
{
goodSamples.Add($"Probability:{result.probability}{Environment.NewLine}Score:{result.score}{Environment.NewLine}{status.Text}{Environment.NewLine}");
}
}
else
{
bad++;
if (badSamples.Count < maxSamples)
{
badSamples.Add($"Probability:{result.probability}{Environment.NewLine}Score:{result.score}{Environment.NewLine}{status.Text}{Environment.NewLine}");
}
}
}
}
Console.WriteLine("****** Sample bad tweets ******");
foreach (string tweet in badSamples)
{
Console.WriteLine(tweet);
}
Console.WriteLine("****** Sample good tweets ******");
foreach (string tweet in goodSamples)
{
Console.WriteLine(tweet);
}
Console.WriteLine($"Total: {bad} bad and {good} good");
}
}