using System;

using System.Collections.Generic;

using System.Linq;

​

public class SentimentAnalyzer

{

    public static string ClassifyText(string text, Dictionary<string, Dictionary<string, int>> trainingData)

    {

        string[] words = text.Split(' ');

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        double positiveProbability = CalculateSentimentProbability(words, "positive", trainingData);

        double negativeProbability = CalculateSentimentProbability(words, "negative", trainingData);

​

        return positiveProbability >= negativeProbability ? "positive" : "negative";

    }

​

    private static double CalculateSentimentProbability(string[] words, string sentiment, Dictionary<string, Dictionary<string, int>> trainingData)

    {

        double probability = 1.0;

        int totalWordsInSentiment = trainingData[sentiment].Values.Sum();

​

        foreach (string word in words)

        {

            if (trainingData[sentiment].ContainsKey(word))

            {

                probability *= (double)trainingData[sentiment][word] / totalWordsInSentiment;

            }

            else

            {

                // If word not in training data, ignore.  This avoids multiplying by zero.

                // Alternatively, you could implement smoothing (Laplace smoothing) here.

            }

        }

​

        return probability;

    }

    public static void Main(string[] args)

    {

        var trainingData = new Dictionary<string, Dictionary<string, int>>

        {

            {

                "positive", new Dictionary<string, int>

                {

                    { "good", 10 },

                    { "great", 8 },

                    { "awesome", 5 },

                    { "bad", 2 }

                }

            },

            {

                "negative", new Dictionary<string, int>

                {

                    { "bad", 7 },

                    { "terrible", 5 },

                    { "awful", 3 },

                    { "good", 2 }

                }

            }

        };

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        string text1 = "good great awesome";

        string prediction1 = ClassifyText(text1, trainingData);

        Console.WriteLine($"Text: {text1}, Prediction: {prediction1}"); // Expected: positive

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        string text2 = "bad terrible awful";

        string prediction2 = ClassifyText(text2, trainingData);

        Console.WriteLine($"Text: {text2}, Prediction: {prediction2}"); // Expected: negative

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        string text3 = "good bad";

        string prediction3 = ClassifyText(text3, trainingData);

        Console.WriteLine($"Text: {text3}, Prediction: {prediction3}"); // Expected: positive (because positive has higher relative frequency for 'good')

​

        string text4 = "unknownword"; // Test with unknown word

        string prediction4 = ClassifyText(text4, trainingData);

        Console.WriteLine($"Text: {text4}, Prediction: {prediction4}"); // Expected: positive.  (Because the prior probabilities are not accounted for and probabilities are very small).

​

        string text5 = "";

        string prediction5 = ClassifyText(text5, trainingData);

        Console.WriteLine($"Text: {text5}, Prediction: {prediction5}");

    }

}

​

​

​

​