public static async Task <List <TweetSentiment> > AnalyzeTweetSentiment(List <TweetRaw> tweets)
{
var tweetsSentiment = new List <TweetSentiment>();
int page = 0;
int itemPerPage = 50;
var temp = new List <TweetRaw>();
temp.AddRange(tweets.Skip(itemPerPage * page++).Take(itemPerPage));
while (temp.Count > 0)
{
var textBatchInput = new TextAnalyticsBatchInput();
foreach (var t in temp)
{
var textAnalysis = new TextAnalyticsInput
{
Id = t.Id.ToString(),
Text = t.Text
};
textBatchInput.Documents.Add(textAnalysis);
}
var sentimentResponse = await AzureSentiment.SentimentV3PreviewPredictAsync(textBatchInput);
Console.WriteLine($"Tweets analyzed:{itemPerPage + itemPerPage * (page - 1)}");
foreach (var document in sentimentResponse.Documents)
{
var tweetSentiment = new TweetSentiment
{
TweetRawId = long.Parse(document.Id),
PositiveScore = document.DocumentScores.Positive,
NeutralScore = document.DocumentScores.Neutral,
NegativeScore = document.DocumentScores.Negative,
Sentiment = Enum.Parse <Degree.Models.DocumentSentimentLabel>(document.Sentiment.ToString()),
};
if (document.Sentences != null && document.Sentences.Count() > 0)
{
foreach (var sentence in document.Sentences)
{
var s = new TweetSentenceSentiment()
{
TweetSentimentId = long.Parse(document.Id),
Length = sentence.Length,
PositiveScore = sentence.SentenceScores.Positive,
NeutralScore = sentence.SentenceScores.Neutral,
NegativeScore = sentence.SentenceScores.Negative,
Offset = sentence.Offset,
Sentiment = Enum.Parse <Degree.Models.SentenceSentimentLabel>(sentence.Sentiment.ToString()),
Warnings = sentence.Warnings
};
tweetSentiment.Sentences.Add(s);
}
}
tweetsSentiment.Add(tweetSentiment);
}
temp.Clear();
temp.AddRange(tweets.Skip(itemPerPage * page++).Take(itemPerPage));
}
return(tweetsSentiment);
}
public static async Task <EntityPII> EntityRecognitionV3PreviewPredictAsync(TextAnalyticsBatchInput inputDocuments)
{
Keys.LoadKey();
using (var httpClient = new HttpClient())
{
httpClient.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", Keys.Azure.TEXT_ANALYSIS_KEY);
var json = JsonConvert.SerializeObject(inputDocuments);
var httpContent = new StringContent(json, Encoding.UTF8, "application/json");
var httpResponse = await httpClient.PostAsync(new Uri(Keys.Azure.TEXT_ANALYSIS_ENTITY_URL), httpContent);
var responseContent = await httpResponse.Content.ReadAsStringAsync();
if (!httpResponse.StatusCode.Equals(HttpStatusCode.OK) || httpResponse.Content == null)
{
throw new Exception(responseContent);
}
return(JsonConvert.DeserializeObject <EntityPII>(responseContent, new JsonSerializerSettings()
{
NullValueHandling = NullValueHandling.Ignore
}));
}
}
public static void Main(string[] args)
{
var count = 1;
Console.WriteLine("=============== Sentiment Analysis ================\n");
Console.WriteLine("The process of computationally identifying and categorizing opinions\nexpressed by the user, especially in order to determine whether the\nuser's attitude towards a particular topic, product, etc. is positive, negative, or neutral.\n");
Console.WriteLine("=============== Start of process ==================\n");
while (true)
{
Console.WriteLine("=============== Prediction Attempt:" + count++ + "===============");
//Console.WriteLine("Prediction Attempt : " + count++);
Console.WriteLine("Enter the Sentiment: ");
var inputSentiment = Console.ReadLine();
var inputDocuments = new TextAnalyticsBatchInput()
{
Documents = new List <TextAnalyticsInput>()
{
new TextAnalyticsInput()
{
Id = "1",
Text = inputSentiment
}
}
};
var sentimentPrediction = TextAnalyticsSentimentClient.SentimentPreviewPredictAsync(inputDocuments).Result;
Console.WriteLine("====================================================================================");
Console.WriteLine("\nSentiment = " + sentimentPrediction.Documents[0].Sentiment + "\nScore = " + sentimentPrediction.Documents[0].Score);
Console.WriteLine("\n====================================================================================\n\n");
}
}
public static async Task <List <TweetEntityRecognized> > AnalyzeTweetEntity(List <TweetRaw> tweets)
{
var tweetsEntity = new List <TweetEntityRecognized>();
int page = 0;
int itemPerPage = 50;
var temp = new List <TweetRaw>();
temp.AddRange(tweets.Skip(itemPerPage * page++).Take(itemPerPage));
while (temp.Count > 0)
{
var textBatchInput = new TextAnalyticsBatchInput();
foreach (var t in temp)
{
var textAnalysis = new TextAnalyticsInput
{
Id = t.Id.ToString(),
Text = t.Text
};
textBatchInput.Documents.Add(textAnalysis);
}
var entityRecognitionResponse = await AzureEntityRecognition.EntityRecognitionV3PreviewPredictAsync(textBatchInput);
Console.WriteLine($"Tweets analyzed:{itemPerPage + itemPerPage * (page - 1)}");
foreach (var document in entityRecognitionResponse.Documents)
{
foreach (var entity in document.Entities)
{
var tweetEntity = new TweetEntityRecognized
{
EntityName = entity.Text,
EntityType = entity.Type,
Length = entity.Length,
Offset = entity.Offset,
Score = entity.Score,
TweetRawId = long.Parse(document.Id)
};
tweetsEntity.Add(tweetEntity);
}
}
temp.Clear();
temp.AddRange(tweets.Skip(itemPerPage * page++).Take(itemPerPage));
}
return(tweetsEntity);
}
public string analyze(string s)
{
var inputDocuments = new TextAnalyticsBatchInput()
{
Documents = new List <TextAnalyticsInput>()
{
new TextAnalyticsInput()
{
Id = "1",
Text = s
}
}
};
var sentimentV3Prediction = TextAnalyticsSentimentV3Client.SentimentV3PreviewPredictAsync(inputDocuments).Result;
string res = "" + sentimentV3Prediction.Documents[0].Sentiment;
return(res);
}
public static void Main(string[] args)
{
var inputDocuments = new TextAnalyticsBatchInput()
{
Documents = new List <TextAnalyticsInput>()
{
new TextAnalyticsInput()
{
Id = "1",
Text = "Hello world. This is some input text."
},
new TextAnalyticsInput()
{
Id = "2",
Text = "It's incredibly sunny outside! I'm so happy."
},
new TextAnalyticsInput()
{
Id = "3",
Text = "Pike place market is not my favorite Seattle attraction."
}
}
};
//If you’re using C# 7.1 or greater, you can use an async main() method to await the function
var sentimentV3Prediction = TextAnalyticsSentimentV3Client.SentimentV3PreviewPredictAsync(inputDocuments).Result;
// Replace with whatever you wish to print or simply consume the sentiment v3 prediction
Console.WriteLine("Document ID=" + sentimentV3Prediction.Documents[0].Id + " : Sentiment=" + sentimentV3Prediction.Documents[0].Sentiment);
Console.WriteLine("Document ID=" + sentimentV3Prediction.Documents[1].Id + " : Sentiment=" + sentimentV3Prediction.Documents[1].Sentiment);
Console.WriteLine("Document ID=" + sentimentV3Prediction.Documents[2].Id + " : Sentiment=" + sentimentV3Prediction.Documents[2].Sentiment);
Console.ReadKey();
}
public static async Task <SentimentV3Response> SentimentV3PreviewPredictAsync(TextAnalyticsBatchInput inputDocuments)
{
using (var httpClient = new HttpClient())
{
httpClient.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", textAnalyticsKey);
var httpContent = new StringContent(JsonConvert.SerializeObject(inputDocuments), Encoding.UTF8, "application/json");
var httpResponse = await httpClient.PostAsync(new Uri(textAnalyticsUrl), httpContent);
var responseContent = await httpResponse.Content.ReadAsStringAsync();
if (!httpResponse.StatusCode.Equals(HttpStatusCode.OK) || httpResponse.Content == null)
{
throw new Exception(responseContent);
}
return(JsonConvert.DeserializeObject <SentimentV3Response>(responseContent, new JsonSerializerSettings()
{
NullValueHandling = NullValueHandling.Ignore
}));
}
}
public static async Task <SentimentResponse> SentimentPreviewPredictAsync(TextAnalyticsBatchInput inputDocuments)
{
//Uri newuri = new Uri(textAnalyticsUrl);
//WebRequest objwebRequest = WebRequest.Create(newuri);
//objwebRequest.Headers.Add("Ocp-Apim-Subscription-Key", textAnalyticsKey);
//HttpWebResponse objwebResponse = (HttpWebResponse)objwebRequest.GetResponse();
//StreamReader objStreamReader = new StreamReader(objwebResponse.GetResponseStream());
//string sResponse = objStreamReader.ReadToEnd();
//List<SentimentResponse> dataList = JsonConvert.DeserializeObject<List<SentimentResponse>>(sResponse);
using (var httpClient = new HttpClient())
{
httpClient.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", textAnalyticsKey);
var httpContent = new StringContent(JsonConvert.SerializeObject(inputDocuments), Encoding.UTF8, "application/json");
var httpResponse = await httpClient.PostAsync(new Uri(textAnalyticsUrl), httpContent);
var responseContent = await httpResponse.Content.ReadAsStringAsync();
if (!httpResponse.StatusCode.Equals(HttpStatusCode.OK) || httpResponse.Content == null)
{
throw new Exception(responseContent);
}
var test = JsonConvert.DeserializeObject(responseContent);
JObject jObject = JObject.Parse(responseContent);
string score = (string)jObject.SelectToken("documents[0].score");
//JArray signInNames = (JArray)jObject.SelectToken("documents");
//foreach (JToken signInName in signInNames)
//{
// type = (string)signInName.SelectToken("score");
//}
SentimentResponse sR = JsonConvert.DeserializeObject <SentimentResponse>(responseContent, new JsonSerializerSettings()
{
NullValueHandling = NullValueHandling.Ignore
});
double scoreCompare = Convert.ToDouble(score);
if (scoreCompare > .5)
{
sR.Documents[0].Sentiment = "Positive";
}
else
{
sR.Documents[0].Sentiment = "Negative";
}
return(sR);
}
}
