Detection of Hate Speech on Social Media Using Sentiment Analysis

Detection of Hate Speech on Social Media Using Sentiment Analysis

ABSTRACT:

The rapid expansion of social media platforms has created an environment where users freely express opinions, emotions, and personal thoughts. While this fosters communication, it also provides fertile ground for the spread of hate speech, abusive language, and harmful expressions. Hate speech on social networks poses serious risks to individuals and communities, contributing to psychological harm, discrimination, and societal tension. As online interactions continue to increase, the development of automated systems capable of identifying and mitigating hateful content has become a critical research area. Sentiment analysis and emotion detection serve as powerful tools for understanding user attitudes and linguistic patterns, making them suitable for addressing the challenge of hate speech detection.

The need for a reliable hate speech detection mechanism is more important than ever, as manual monitoring is neither scalable nor consistent across large datasets. Automated analysis helps organizations, researchers, and platforms enforce community guidelines, improve user safety, and reduce the impact of harmful communication. Integrating sentiment analysis with emotion classification enhances accuracy by not only measuring the polarity of text but also revealing the underlying emotional intensity behind user expressions. This enriched analysis supports more precise categorization of hateful content, allowing the system to differentiate between normal negative opinions and explicit hate speech.

To address this need, the project titled “Detection of Hate Speech on Social Media Using Sentiment Analysis” presents a comprehensive framework developed using Java, with JSP, CSS, and JavaScript for the frontend and MySQL for data storage. The proposed system operates in two modes.

In the first mode, the application processes a Facebook comments dataset in CSV format containing 2,097 records. The system performs preprocessing and evaluates each comment individually. It computes the Sentiment Score, predicts the Sentiment Type, calculates the Emotion Score, and identifies the Emotion Type across categories such as Happiness, Sadness, Anger, Fear, Disgust, and Surprise. The system also extracts hate-related keywords and assigns a final label indicating whether the comment contains hate speech. The complete set of analytical results is visually represented through a static graph that summarizes dataset trends and distribution.

In the second mode, the system supports real-time interaction through two user roles: Admin and User. Users can post comments within the application interface, and each submitted comment undergoes sentiment and emotion processing similar to dataset-based analysis. In the Admin module, the system displays detailed analytical outputs including Sentiment Score, Sentiment Type, Emotion Score, Emotion Type, and detected Hate Words.

Through its dual-mode architecture combining dataset-level analysis with live user comment evaluation, the system provides a scalable, accurate, and efficient framework for detecting hate speech on social media using sentiment and emotion analysis techniques.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In existing approaches to identifying hate speech on social media, the primary focus was on manual moderation and basic keyword-based filtering techniques. Social media platforms and researchers often relied on predefined lists of abusive or offensive words to detect inappropriate content. When a comment contained one or more entries from these lists, it was flagged as potentially harmful. This rule-based approach offered a simple mechanism to monitor online conversations and provided a starting point for addressing harmful communication.
• In the existing system, text analysis methods also made use of basic natural language processing (NLP) techniques, where text was tokenized and evaluated using direct pattern matching or frequency-based models. In some systems, machine learning models trained on small labeled datasets were used to classify comments based on linguistic cues. These existing systems typically processed dataset inputs in a batch format, allowing large collections of social media comments to be reviewed and categorized. They helped analysts study the general distribution of negative or harmful expressions across a platform.
• Another component of the existing systems was sentiment analysis focused primarily on polarity. This provided insight into user attitudes but did not capture deeper emotional indicators or contextual cues related to hate speech. Basic sentiment scoring methods enabled researchers to understand overall trends within social media datasets and offered foundational information for further investigation.
• Overall, the existing systems played an important role in initiating structured efforts toward monitoring harmful language on digital platforms. They provided initial insights, supported dataset-level evaluations, and established essential groundwork on which advanced hate speech detection techniques could be developed.

DISADVANTAGES OF EXISTING SYSTEM:

• Limited Accuracy Due to Keyword-Based Detection: The existing system mainly depended on predefined lists of abusive or offensive words. This approach often failed to identify hate speech expressed indirectly, through sarcasm, slang, altered spellings, or contextual cues. As a result, the accuracy of detection was restricted, especially when users intentionally manipulated language to bypass filters.
• Inability to Understand Context and Emotion: The existing system did not incorporate sentiment polarity or emotional depth, making it difficult to differentiate between general negative statements and actual hate speech. Without context awareness, many comments were either overlooked or incorrectly flagged, reducing the reliability of the system.
• No Support for Multi-Category Analysis: The existing system did not analyze multiple linguistic features such as sentiment score, emotion classification, or hate word identification. Since it focused only on surface-level patterns, it lacked the capability to provide detailed categorization or deeper insights into user behavior and communication patterns.
• Manual Moderation Burden: In the existing system, due to the limitations of automated keyword filtering, human moderators were required to manually review a large portion of flagged comments. This increased workload, consumed time, and resulted in inconsistent decision-making across different reviewers.
• Inability to Process Real-Time User Inputs: The existing system operated solely on static datasets and could not analyze comments posted by users in real time. This prevented timely identification of harmful content and reduced the practical effectiveness of the system in dynamic social media environments.
• Limited Scalability for Large Data Volumes: With social media growth producing millions of comments daily, existing filtering mechanisms were not optimized for large-scale processing. Their simplicity restricted scalability, making them inefficient for handling continuously expanding datasets.
• Lack of Visual Analytical Insights: The existing system did not provide graphical representations or interactive dashboards to observe trends, distributions, or behavioral patterns within datasets. Without visualization, interpreting results or making data-driven decisions became more difficult.

PROPOSED SYSTEM:

• The proposed system, titled “Detection of Hate Speech on Social Media Using Sentiment Analysis”, is designed as a web-based application developed using Java as the core programming language, JSP, CSS, and JavaScript for the frontend, and MySQL as the backend database. The system focuses on processing social media comments, performing sentiment and emotion analysis, detecting hate-related terms, and labeling records accordingly. The overall framework combines text preprocessing, feature extraction, sentiment scoring, emotion classification, hate word identification, and result visualization within a single integrated platform.
• The proposed system operates in two distinct modes of application. In the first mode, it works with an offline Facebook comments dataset in CSV format containing 2,097 records. The administrator uploads this dataset through the web interface. Each comment in the dataset undergoes a series of preprocessing steps such as cleaning, tokenization, and normalization. After preprocessing, the system computes the Sentiment Score for each comment and predicts the corresponding Sentiment Type (for example, positive, negative, or neutral). In addition, it calculates the Emotion Score and determines the Emotion Type across categories such as Happiness, Sadness, Anger, Fear, Disgust, and Surprise. The system further identifies specific Hate Words present in the text and assigns a final label indicating whether the comment is hate speech or not. Once all records are processed, the system stores the fully annotated dataset in the database and generates a static graph to display the overall distribution and patterns observed in the processed data.
• In the second mode, the system supports interactive usage with two main entities: Admin and User. Users can register or log in to the application and post comments through a JSP-based interface. When a user submits a comment, the system immediately performs preprocessing and runs sentiment and emotion analysis similar to the dataset mode. The processed details are stored in a separate table dedicated to user-generated content. The Admin module provides a secure backend interface where the administrator can monitor all user-submitted comments along with their analytical results. For each comment, the system displays the Sentiment Score, Sentiment Type, Emotion Score, Emotion Type, and the list of detected Hate Words. Examples of such hate words include terms like “fuck,” “assassin,” “stupidfucker,” “criminal,” and other abusive expressions configured within the system.
• Overall, the proposed system integrates dataset-based batch processing and live user comment analysis within a unified architecture. It uses MySQL tables to manage raw comments, processed features, user information, and analysis outputs. JSP pages are utilized for dataset upload, user comment submission, admin monitoring, and graph visualization. Through these components, the system implements a complete workflow starting from data input (either CSV or user comment), followed by sentiment and emotion computation, hate word detection, labeling, storage, and final presentation of the analyzed information in a structured and organized manner.

ADVANTAGES OF PROPOSED SYSTEM:

• Automated Detection of Hate Speech: The proposed system eliminates the need for manual monitoring by automatically identifying hate speech using sentiment analysis, emotion classification, and hate word detection. This improves consistency, reduces human error, and ensures faster analysis of large volumes of text.
• Dual-Mode Processing Capability: The proposed system supports both dataset-based analysis and real-time user comment processing. This dual functionality allows it to handle offline bulk datasets as well as live interactions, making the platform flexible for academic, research, and operational environments.
• Enhanced Understanding Through Sentiment and Emotion Analysis: By providing both sentiment score/type and emotion score/type, the proposed system offers a deeper understanding of the emotional tone behind user comments. This multi-dimensional analysis helps in more accurately identifying harmful or aggressive language.
• Effective Hate Word Identification: The proposed system maintains a predefined lexicon of hate words and detects their presence within comments. This targeted detection approach strengthens the accuracy of hate speech classification by focusing on explicit abusive expressions.
• User-Friendly Interface for Admin and Users: In the proposed system, the application includes a simple comment-posting interface for users and a detailed monitoring dashboard for admins. The clear presentation of processed results such as sentiment, emotion, hate words, and labels enhances usability and ensures smooth system navigation.
• Structured Storage and Efficient Data Management: In the proposed system, all raw comments and processed outputs are stored securely in MySQL databases. This structured storage enables easy retrieval, efficient processing, and systematic analysis of results for review or reporting purposes.
• Visual Representation of Analytical Results: The proposed system generates graphical summaries that illustrate trends such as hate speech distribution, sentiment breakdown, and dataset statistics. These visual insights help administrators better understand patterns and make informed decisions.
• Scalable and Extensible System Architecture: The proposed system modular design, built using Java, JSP, and MySQL, allows easy scaling and extension. Additional features, datasets, or analytical modules can be integrated without disrupting core functionality.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 20 GB.
• Monitor : 15’’ LED.
• Input Devices : Keyboard, Mouse.
• Ram : 8 GB.

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10/11.
• Coding Language : Java.
• Frontend : JSP, CSS, JavaScript.
• JDK Version : JDK 23.0.1.
• IDE Tool : Apache Netbeans IDE 24.
• Tomcat Server Version : Apache Tomcat 9.0.84
• Database : MySQL.

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Frequently Asked Questions (FAQ’s) and Answers

1. What is the main objective of this project?

The main objective of this project is to automatically detect hate speech in social media comments by performing sentiment analysis, emotion classification, and hate word identification. The system evaluates text to determine its emotional tone, sentiment polarity, and presence of abusive or hateful expressions.

2. Which technologies are used to develop the system?

The project is developed using Java for backend logic, JSP, CSS, and JavaScript for frontend design, and MySQL for database management. Apache Tomcat is used as the server for deploying and running the web application.

3. How many modes of operation does the system support?

The system works in two modes: • Dataset Mode – Admin uploads a Facebook comments dataset (.csv), and the system processes all 2,097 records automatically. • Live User Mode – Registered users post comments, and the system analyzes them instantly.

4. What type of dataset is used in this project?

A Facebook comments dataset in CSV format containing 2,097 text records is used. The dataset includes various comments, which are processed for sentiment, emotion, hate words, and final labeling.

5. What analytical outputs does the system generate?

For each processed comment, the system generates: • Sentiment Score • Sentiment Type (Positive/Negative/Neutral) • Emotion Score • Emotion Type (Happiness, Sadness, Anger, Fear, Disgust, Surprise) • Detected Hate Words • Final Label (Hate Speech or Non-Hate Speech)

6. How does the system detect hate speech?

The system detects hate speech by combining: • Sentiment polarity • Emotional intensity • Presence of predefined hate keywords • Text preprocessing and analysis rules If abusive or hate-related words are found, or if sentiment/emotion indicators suggest hostility, the comment is labeled as hate speech.

7. Can users interact with the system in real time?

Yes. In user mode, registered users can post comments in real time. Each submitted comment is immediately analyzed, and results are stored for admin review.

8. What role does the Admin play in the system?

The Admin can: • Upload and process the dataset • View all processed comments and their analytical results • Monitor user-posted comments • Review sentiment, emotion, hate words, and labels • View graphical summary reports

9. How are graphs generated in the system?

Graphs are generated using chart libraries in the JSP interface. They visualize metrics such as: • Total comments vs. hate comments • Sentiment distribution • Other analytical summaries from processed data These graphs help in quickly understanding dataset trends.

10. How are comments preprocessed before analysis?

The preprocessing module performs: • Removal of special characters • Lowercasing text • Tokenization • Cleaning and normalization This ensures accurate sentiment and emotion detection.

11. How are hate words identified?

The system checks each comment against a predefined list of hate words such as “fuck,” “assassin,” “stupidfucker,” “criminal,” “negro,” “nastybitch,” “bullshit,” “lesbian,” “homosexual,” etc. Any matches found are recorded and displayed to the admin.

12. Does the system store user information and analytics securely?

Yes. User accounts and analytical results are securely stored in MySQL tables. Access to sensitive data is restricted to the Admin module.

13. What are the expected outputs after dataset processing?

After processing all dataset records, the system generates: • A fully annotated database table • Labeled comments • Sentiment and emotion summaries • Static graphs showing trends and distributions

14. What makes this system useful for social media monitoring?

The system automates detection of harmful or abusive content, supports real-time monitoring, and provides detailed sentiment and emotion insights, making it a powerful tool for improving online communication safety.