Understand EmotionsBehind Every Word

using LMKit.TextAnalysis;

var model = LM.LoadFromModelID(
    "lm-kit-sentiment-analysis-2.0-1b");

var sentiment = new SentimentAnalysis(model)
{
    NeutralSupport = true
};

// Single classification
var result = sentiment.GetSentimentCategory(
    "This product exceeded all my expectations!");

Console.WriteLine($"Sentiment: {result}");
// Output: Positive

// Get detailed confidence scores
var scores = sentiment.GetConfidenceScores(
    "The delivery was acceptable.");

Console.WriteLine($"Positive: {scores.Positive:P1}");
Console.WriteLine($"Negative: {scores.Negative:P1}");
Console.WriteLine($"Neutral:  {scores.Neutral:P1}");
// Output: Positive: 23.4%
//         Negative: 12.1%
//         Neutral:  64.5%

using LMKit.TextAnalysis;

var model = LM.LoadFromModelID("phi-3.5-mini");
var sarcasm = new SarcasmDetection(model);

// Quick boolean check
bool isSarcastic = sarcasm.IsSarcastic(
    "Oh wonderful, another Monday morning meeting.");

Console.WriteLine($"Sarcastic: {isSarcastic}");
// Output: Sarcastic: True

// Get confidence score
float score = sarcasm.GetSarcasmScore(
    "Best customer service experience ever!");

Console.WriteLine($"Sarcasm confidence: {score:P1}");
// Output: Sarcasm confidence: 78.4%

// Combined analysis workflow
var sentiment = new SentimentAnalysis(model);
string text = "Sure, I just love waiting 2 hours.";

var sentimentResult = sentiment.GetSentimentCategory(text);
var sarcasmResult = sarcasm.IsSarcastic(text);

// Adjust interpretation based on sarcasm
var actualSentiment = sarcasmResult 
    ? InvertSentiment(sentimentResult) 
    : sentimentResult;