Social media platforms generate vast amounts of unstructured data daily. Brands, marketers, and researchers are increasingly interested in understanding how users feel about products, services, or events. Sentiment analysis, powered by AI and machine learning, helps extract actionable insights from social media posts.

In this article, we will explore how to build a full-stack web application for sentiment-based social media analytics using ASP.NET Core for the backend, Angular for the frontend, and AI for sentiment analysis. We will focus on real-world best practices, scalability, and maintainability.

Table of Contents

  1. Introduction to Social Media Sentiment Analysis

  2. Core Features of the Analytics App

  3. Architecture Overview

  4. Setting Up ASP.NET Core Backend

  5. Integrating Social Media APIs

  6. Implementing Sentiment Analysis

  7. Building the Angular Frontend

  8. Data Visualization and Dashboards

  9. Authentication and Security

  10. Performance Optimization

  11. Testing and Deployment

  12. Conclusion

1. Introduction to Social Media Sentiment Analysis

Sentiment analysis is the process of detecting positive, negative, or neutral emotions in textual data. For social media, this involves:

A full-stack app combines data collection, processing, analysis, and visualization into a single platform that allows users to monitor social media sentiment in real time.

2. Core Features of the Analytics App

A production-ready social media sentiment analysis platform should include:

  1. Data Collection: Fetch posts from APIs like Twitter, Facebook, or Instagram.

  2. Sentiment Analysis Engine: AI-powered classification into positive, negative, or neutral.

  3. Interactive Dashboards: Visualize trends with charts and graphs.

  4. Filters and Search: Filter posts by date, user, keyword, or sentiment.

  5. Alerts and Notifications: Notify when sentiment crosses thresholds.

  6. User Authentication: Secure access to analytics.

  7. Export Options: Download reports in CSV or PDF.

3. Architecture Overview

A scalable architecture includes:

Backend (ASP.NET Core)

Frontend (Angular)

AI & NLP

Storage

4. Setting Up ASP.NET Core Backend

Create a new ASP.NET Core Web API:

dotnet new webapi -n SentimentAnalyticsAPI
cd SentimentAnalyticsAPI

Install essential packages:

dotnet add package Microsoft.EntityFrameworkCore.SqlServer
dotnet add package Microsoft.AspNetCore.Authentication.JwtBearer
dotnet add package Microsoft.Extensions.Http

Configure JWT authentication in Program.cs:

builder.Services.AddAuthentication(JwtBearerDefaults.AuthenticationScheme)
    .AddJwtBearer(options =>
    {
        options.TokenValidationParameters = new TokenValidationParameters
        {
            ValidateIssuer = true,
            ValidateAudience = true,
            ValidateLifetime = true,
            ValidIssuer = builder.Configuration["Jwt:Issuer"],
            ValidAudience = builder.Configuration["Jwt:Audience"],
            IssuerSigningKey = new SymmetricSecurityKey(
                Encoding.UTF8.GetBytes(builder.Configuration["Jwt:Key"]))
        };
    });

5. Integrating Social Media APIs

a. Twitter API Example

public class TwitterService
{
    private readonly HttpClient _http;

    public TwitterService(HttpClient http)
    {
        _http = http;
    }

    public async Task<List<Tweet>> GetTweetsAsync(string hashtag)
    {
        var response = await _http.GetAsync($"https://api.twitter.com/2/tweets/search/recent?query={hashtag}");
        response.EnsureSuccessStatusCode();
        var json = await response.Content.ReadAsStringAsync();
        return JsonSerializer.Deserialize<List<Tweet>>(json);
    }
}

6. Implementing Sentiment Analysis

You can implement sentiment analysis using ML.NET, a Python API, or external AI services.

a. Using ML.NET

public class SentimentModel
{
    public string Text { get; set; }
    public bool Sentiment { get; set; }
}

var mlContext = new MLContext();
var data = mlContext.Data.LoadFromEnumerable(tweets);

var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", nameof(SentimentModel.Text))
    .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Sentiment", featureColumnName: "Features"));

var model = pipeline.Fit(data);

b. Using Python AI API

var response = await _http.PostAsJsonAsync("http://localhost:5000/analyze", tweetText);
var sentiment = await response.Content.ReadFromJsonAsync<SentimentResult>();

7. Building the Angular Frontend

Create Angular project:

ng new sentiment-analytics --routing --style=scss
cd sentiment-analytics
ng add @angular/material

a. Service for Analytics API

@Injectable({ providedIn: 'root' })
export class AnalyticsService {
  constructor(private http: HttpClient) {}

  getSentimentData(filter: any) {
    return this.http.post('/api/analytics/sentiments', filter);
  }

  getTrendingHashtags() {
    return this.http.get('/api/analytics/trending');
  }
}

b. Components

8. Data Visualization and Dashboards

Use Chart.js or ngx-charts for interactive visualizations:

public sentimentChartData = {
  labels: ['Positive', 'Neutral', 'Negative'],
  datasets: [
    {
      data: [120, 50, 30],
      backgroundColor: ['#4caf50', '#ffeb3b', '#f44336']
    }
  ]
};

9. Authentication and Security

[Authorize(Roles = "Admin,Analyst")]
[HttpGet("trending")]
public IActionResult GetTrendingHashtags() => Ok(_analyticsService.GetTrendingHashtags());

10. Performance Optimization

11. Testing and Deployment

Testing

Deployment

12. Conclusion

Building a full-stack sentiment-based social media analytics platform combines modern web technologies, AI, and data visualization. Key takeaways:

With this architecture, developers can build scalable and maintainable platforms for brands, researchers, or marketers to monitor sentiment trends in real-time and make data-driven decisions.