public SentimentSeries GetSentimentByKeyword(string keyword, TimeLibrary.TimeInterval groupingInterval, DateTime startDate, DateTime endDate)
{
var tweets = _TweetRepo.GetTweetsByKeyword(keyword, startDate, endDate);
var sentiment = _SentimentAnalyzer.GetSentiment(tweets, groupingInterval);
var sentimentSeries = new SentimentSeries()
{
Keywords = new[] { keyword },
Sentiment = sentiment,
};
return(sentimentSeries);
}
public SentimentSeries GetSentimentByConfigId(long configId, TimeLibrary.TimeInterval groupingInterval, DateTime startDate, DateTime endDate)
{
var tweets = _TweetRepo.GetTweetsByConfigId(configId, startDate, endDate);
var sentiment = _SentimentAnalyzer.GetSentiment(tweets, groupingInterval);
var config = _ConfigRepository.GetConfigurationById(configId);
var sentimentSeries = new SentimentSeries()
{
Keywords = new[] { config.Text },
Sentiment = sentiment,
};
return(sentimentSeries);
}
/// <summary>
/// Gets the sentiment for a set of tweets. Uses the Naive Bayes method.
/// </summary>
/// <param name="tweets"></param>
/// <returns></returns>
public Dictionary <DateTime, SentimentInfo> GetSentiment(IEnumerable <Tweet> tweets, TimeLibrary.TimeInterval groupingInterval)
{
var sentimentScores = new Dictionary <DateTime, SentimentInfo>();
//// This will group the tweets by hour
//var tweetsGroupedByHour = tweets
// .GroupBy(t => new DateTime(t.CreatedAt.Year, t.CreatedAt.Month, t.CreatedAt.Day, t.CreatedAt.Hour, 0, 0));
// This will group the tweets by month
var tweetsGroupedByHour = tweets
.GroupBy(t => new DateTime(t.CreatedAt.Year, t.CreatedAt.Month, 1, 0, 0, 0));
// foreach group of tweets
foreach (var tweetGroup in tweetsGroupedByHour)
{
var numberOfTweets = tweetGroup.Count();
// calculate the sum of sentiment of each tweet in the group
var sentiment = tweetGroup.Sum(t => CalculateSentiment(t)) / numberOfTweets;
// add the datetime and the sentiment info to the sentiment dictionary
sentimentScores
.Add(
tweetGroup.Key,
new SentimentInfo()
{
Sentiment = sentiment,
Weight = numberOfTweets
});
}
return(sentimentScores);
}
public Dictionary <DateTime, SentimentInfo> GetSentiment(IEnumerable <Tweet> tweets, TimeLibrary.TimeInterval groupingInterval)
{
// If there are no tweets, return empty Dictionary
if (tweets.Any() == false)
{
return(new Dictionary <DateTime, SentimentInfo>());
}
var sentimentScores = new Dictionary <DateTime, SentimentInfo>();
//List<string> groupTweets = new List<string>();
//List<bool> groupSentiment = new List<bool>();
//// This will group the tweets by hour
//var tweetsGroupedByHour = tweets
// .GroupBy(t => new DateTime(t.CreatedAt.Year, t.CreatedAt.Month, t.CreatedAt.Day, t.CreatedAt.Hour, 0, 0));
var scores = this.runPrediction(tweets.Select(t => t.Text).ToList());
var tweetsWithScore = tweets.Zip(scores, (t, s) => new { Score = s, Tweet = t });
// This will group the tweets by month
var tweetsGrouped = tweetsWithScore.GroupByTime(t => t.Tweet.CreatedAt, groupingInterval);
// .GroupBy(t => new DateTime(t.Tweet.CreatedAt.Year, t.Tweet.CreatedAt.Month, 1, 0, 0, 0));
// foreach group of tweets
foreach (var tweetGroup in tweetsGrouped)
{
var numberOfTweets = tweetGroup.Count();
//// calculate the sum of sentiment of each tweet in the group
var sentiment = 0.00;
var groupSentiment = tweetGroup.Select(g => g.Score);
foreach (var prediction in groupSentiment)
{
//prediction being set to true means that a positive prediction was made
if (prediction == true)
{
sentiment += 1;
}
}
sentiment /= numberOfTweets;
// add the datetime and the sentiment info to the sentiment dictionary
sentimentScores
.Add(
tweetGroup.Key,
new SentimentInfo()
{
Sentiment = sentiment,
Weight = numberOfTweets
});
}
return(sentimentScores);
}
public static IEnumerable <IGrouping <DateTime, TSource> > GroupByTime <TSource>(this IEnumerable <TSource> source,
Func <TSource, DateTime> selector, TimeLibrary.TimeInterval interval)
{
var firstTime = new DateTime(source.Min(e => selector(e).Ticks));
//var t1 = firstTime.Floor(interval).Ticks;
var groups = source.GroupBy(e =>
{
// group# = floor((t - t1) / interval)
//int groupNumber = (int)((selector(e).Ticks - t1) / interval.Ticks);
//DateTime dateGroup = new DateTime(t1 + (groupNumber * interval.Ticks));
DateTime dateGroup = selector(e).Floor(interval);
return(dateGroup);
});
return(groups);
}
