NLP_MPR_PPT Hate Speech Recognition.pptx

In the age of digital communication, the rise of hate speech and online toxicity has become a
pressing issue, threatening the safety and well-being of internet users.

NLP technology is a critical tool for addressing this problem, as it enables automated identification
and categorization of hate speech within vast volumes of text data, a task often impossible for human
moderators to handle effectively.

Hate speech is a complex and evolving issue, often manifesting in subtle and context-dependent
ways. NLP models are designed to understand the nuances of language and context, making them
essential for recognizing and addressing these subtler forms of hate speech.

The application of NLP in hate speech detection is not a one-time solution but an ongoing process.
Machine learning algorithms continually adapt to the changing landscape of online hate speech,
allowing for real-time detection and prevention to maintain a safer online environment.

1. NLP Mini Project: Hate Speech Recognition Raj Tandon D17C/62 Anurag Tripathi D17C/68 Param Pandey D17C/50 Sunny Bhatia D17C/72

2. Chapter 1: Introduction Chapter 2: Literature Survey Chapter 3: Requirements Chapter 4: Proposed Design Chapter 5: Implementation Chapter 6: Result Analysis Chapter 7: Conclusion Index

3. Introduction In the age of digital communication, the rise of hate speech and online toxicity has become a pressing issue, threatening the safety and well-being of internet users. NLP technology is a critical tool for addressing this problem, as it enables automated identification and categorization of hate speech within vast volumes of text data, a task often impossible for human moderators to handle effectively. Hate speech is a complex and evolving issue, often manifesting in subtle and context-dependent ways. NLP models are designed to understand the nuances of language and context, making them essential for recognizing and addressing these subtler forms of hate speech. The application of NLP in hate speech detection is not a one-time solution but an ongoing process. Machine learning algorithms continually adapt to the changing landscape of online hate speech, allowing for real-time detection and prevention to maintain a safer online environment.

4. Literature Survey Title Authors Date of publication Abstract Ethos: An Online Hate Speech Dataset Stamatis Karlos 2021 Rising online hate speech exploits social media, requires robust detection systems like 'ETHOS' dataset to comply with laws and preserve online quality and safety. Hate Speech Recommendation System using NLP and Deep Learning Sagar Mujumale, Prof Nagaraju Bogiri 2022 Rising online hate speech, tied to racial prejudice, requires NLP-based detection. Government initiatives are addressing this, driven by the internet's rapid expansion.

5. A systematic review of hate speech automatic detection using natural language processing Md Saroar Jahan, Mourad Oussalah 2023 Multiplying social media platforms pose a challenge in hate speech detection. This paper reviews literature, focusing on NLP and deep learning methods for potential solutions. A Survey on Hate Speech Detection using Natural Language Processing Anna Schmidt, Michael Wiegand 2017 This paper surveys hate speech detection in the context of growing social media content, emphasizing NLP techniques and discussing their limitations.

6. Intelligent detection of hate speech in Arabic social network: A machine learning approach Ibrahim Aljarah, Maria Habib, Neveen Hijazi, Hossam Faris, Raneem Qaddoura, Bassam Hammo, Mohammad Abushariah, Mohammad Alfawareh 2020 Growing cyber hate speech threatens social cohesion, especially in the Arab region. This article uses NLP and ML on Arabic tweets to detect hate speech, achieving best results with RF and TF-IDF features.

7. Requirements Data Requirements: Labeled Dataset : A dataset containing textual content labeled into categories such as "Hate Speech", "Offensive Speech", and "Neutral". For this project, the dataset is assumed to be in CSV format (e.g., HateSpeechData.csv ). Data Quality : The dataset should be free from inconsistencies and biases. It's crucial that the dataset represents various demographic groups, languages, and cultures to avoid model biases. Test Data : Apart from training data, a separate set of labeled data is essential for validating and testing the model's performance. Technical Requirements: Programming Environmen t: Python programming environment set up with necessary libraries and dependencies. Libraries : Essential Python libraries including: pandas and numpy for data manipulation. sklearn for machine learning operations. nltk for natural language processing tasks.

8. Proposed Design Pre-processing & Feature Extraction Module: This component is responsible for: Cleaning and sanitizing the text: It will standardize the textual data by converting it to lowercase, removing URLs, HTML tags, punctuations, and any unnecessary whitespace. Removing stopwords: Common words that don't add significant meaning to the text will be removed. Stemming: Words will be reduced to their root form. Vectorization: The cleaned and stemmed text will be transformed into numerical vectors using CountVectorizer. Modeling & Prediction Module : This central component will: Train a machine learning model (BaggingClassifier) on the processed data. Provide functionalities for predicting the category of new, unseen textual data. Evaluation & Optimization Module: This module will: Use metrics such as accuracy , F1-score, precision, and recall to evaluate the performance of the trained model. Allow for hyperparameter tuning and model optimization based on evaluation metrics.

9. Implementation The NLP model has been implemented using Google Colab IDLE, along with the NLP model which uses the Bagging Classifier algorithm for predicting if the text is a hate speech. The dataset can be found here . Notebook Link

10. Result Analysis After creating a classifier model for our model, we used metrics such as F1-Score, Accuracy etc. to check the accuracy of our model. Here are the results we got

11. Conclusion The advent of the digital era has revolutionized human interaction, bringing forth myriad opportunities and challenges. One significant challenge, as evidenced by the growing online platforms, is the rise of hate speech and offensive content. While these platforms have granted voices to the masses, ensuring that these voices foster healthy dialogue instead of promoting divisiveness and hate becomes paramount. The Automated Hate Speech Detection system presented in this study is a testament to the capabilities of Natural Language Processing (NLP) and machine learning in addressing this concern. Through meticulous data processing, innovative modeling techniques, and robust evaluation methods, we've strived to create a system that is both accurate and efficient. The system's modular design ensures adaptability, allowing for continuous improvements as technological advancements emerge and datasets grow.