SYSTEM FOR COMPARATIVE ANALYSIS OF HATE SPEECH DETECTION TECHNIQUES

Abstract

ABSTRACT A system (100) for comparative analysis of hate speech detection techniques, comprising a processing module (102) for text data cleaning, a feature extraction module (104) for deriving linguistic features, a model evaluation module (106) with machine learning algorithms for hate speech classification, and an analytics module (108) for visualizing model performance across metrics. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to field of automated hate speech detection, particularly focusing on the comparative analysis of various machine learning (ML) and artificial intelligence (AI) methods in identifying and categorizing hate speech from text data.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] With the surge of social media and online interactions, hate speech detection has become crucial in maintaining safe digital environments. Traditional methods are often rule- , Claims:1. A system (100) for comparative analysis of hate speech detection techniques, comprising:
a processing module (102) for text data cleaning,
a feature extraction module (104) for deriving linguistic features,
a model evaluation module (106) with machine learning algorithms for hate speech classification, and
an analytics module (108) for visualizing model performance across metrics.

2. The system (100) as claimed in claim 1, wherein the pre-processing module (102) removes punctuation, standardizes casing, and tokenizes text data to enhance data quality for feature extraction.

3. The system (100) as claimed in claim 1, wherein the feature extraction module (104) applies TF-IDF, word embedding, and sentiment analysis to produce interpretable features for hate speech classification.

4. The system (100) as claimed in claim 1, wherein the analytics module (108) visualizes model performance through precision, recall, accuracy, and F1 score metrics, highlighting trends across different datasets and hate speech

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