using System;

using System.Collections.Generic;

using System.Linq;

​

public class SentimentAnalyzer

{

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

    {

        if (string.IsNullOrWhiteSpace(text) || trainingData == null || trainingData.Count == 0)

        {

            return "positive"; // Default to positive if no training data or empty text

        }

​

        var sentiments = trainingData.Keys.ToList();

        var probabilities = new Dictionary<string, double>();

​

        // Calculate vocabulary size (unique words across all sentiments)

        var vocabulary = new HashSet<string>();

        foreach (var sentimentData in trainingData.Values)

        {

            vocabulary.UnionWith(sentimentData.Keys);

        }

        int vocabularySize = vocabulary.Count;

​

        foreach (var sentiment in sentiments)

        {

            if (!trainingData.ContainsKey(sentiment)) continue;

​

            var wordsInSentiment = trainingData[sentiment];

            double totalWordsInSentiment = wordsInSentiment.Values.Sum();

            double logLikelihood = 0.0;

​

            // Prior probability

            double prior = Math.Log((double)trainingData[sentiment].Count / vocabularySize);

​

            var textWords = text.Split(' ');

​

            foreach (var word in textWords)

            {

                if (string.IsNullOrWhiteSpace(word)) continue;

​

                // Laplace smoothing

                double wordCountInSentiment = wordsInSentiment.ContainsKey(word) ? wordsInSentiment[word] : 0;

                double wordProbability = (wordCountInSentiment + 1) / (totalWordsInSentiment + vocabularySize);

                logLikelihood += Math.Log(wordProbability);

            }

            probabilities[sentiment] = prior + logLikelihood;

        }

​

        // Find sentiment with highest probability

        string predictedSentiment = "positive"; // Default to positive in case of a tie

        double maxProbability = double.NegativeInfinity;

        foreach (var kvp in probabilities)

        {

            if (kvp.Value > maxProbability)

            {

                maxProbability = kvp.Value;

                predictedSentiment = kvp.Key;

            }

        }

​

        return predictedSentiment;

    }

     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 }

                }

            }

        };

​

        string text1 = "this is a good and great movie";

        string sentiment1 = ClassifyText(text1, trainingData);

        Console.WriteLine($"'{text1}' is classified as: {sentiment1}"); // Expected: positive

​

        string text2 = "this is a bad and terrible movie";

        string sentiment2 = ClassifyText(text2, trainingData);

        Console.WriteLine($"'{text2}' is classified as: {sentiment2}"); // Expected: negative

​

        string text3 = "this is a good and bad movie"; //text in both

        string sentiment3 = ClassifyText(text3, trainingData);

        Console.WriteLine($"'{text3}' is classified as: {sentiment3}"); // Expected: positive (Laplace causes this)

​

        string text4 = "unknown words test";

        string sentiment4 = ClassifyText(text4, trainingData);

        Console.WriteLine($"'{text4}' is classified as: {sentiment4}");

​

        //Test with one sentiment missing

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

        {

            {

                "positive", new Dictionary<string, int>

                {

                    { "good", 10 },

                    { "great", 8 },

                    { "awesome", 5 },

                    { "bad", 2 }

                }

            }

        };

        string text5 = "this is a bad and terrible movie";

        string sentiment5 = ClassifyText(text5, trainingDataOneSentiment);

        Console.WriteLine($"'{text5}' is classified as: {sentiment5}");

    }

}