Hate Speech Detection And Sentiment Analysis Using Machine Learning And Deep

Hate speech has become a prominent issue in today's digital age. With the rise of social media platforms and online forums, hateful and offensive content has become increasingly prevalent. Detecting hate speech and understanding its sentiment is crucial in maintaining a safe and inclusive online environment. In this article, we will explore how machine learning and deep learning techniques can be used to address this problem.

Understanding Hate Speech

Hate speech refers to any form of communication, whether written, spoken, or symbolic, that offends, threatens, or insults individuals or groups based on attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender. Identifying hate speech is challenging due to its subjective nature and the evolving nature of language. Machine learning algorithms can help analyze large amounts of text data and identify patterns associated with hate speech.

Sentiment Analysis

Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone behind a series of words. In the context of hate speech detection, sentiment analysis can be used to understand the intent and impact of offensive language. By analyzing the sentiment associated with specific words or phrases, we can gauge the level of harm they may cause.

HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI

by Vivian Siahaan (Kindle Edition)

4.6 out of 5

Language	:	English
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Print length	:	221 pages
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Machine Learning for Hate Speech Detection

Machine learning algorithms can be trained on large datasets of labeled hate speech to automatically identify offensive content. These algorithms can learn patterns and relationships between words, allowing them to differentiate between hate speech and non-offensive language. Features such as n-grams, word frequency, and part-of-speech can be extracted from the text to train classification models, such as support vector machines (SVM) or recurrent neural networks (RNN).

Deep Learning for Hate Speech Detection

Deep learning, a subset of machine learning, utilizes artificial neural networks to extract abstract representations of data. Deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have shown promising results in hate speech detection. These models can capture the context and semantics of the text, enabling more accurate classification.

The Challenges

Although machine learning and deep learning techniques have shown promise in hate speech detection, several challenges still remain. One of the major challenges is the cultural and contextual nuances of hate speech. Words and phrases can have different meanings depending on the cultural background and context in which they are used. Adapting machine learning models for different languages and cultural settings is crucial for effective hate speech detection.

Hate speech detection and sentiment analysis using machine learning and deep learning techniques have the potential to combat online hate speech and create a safer digital environment. As technology advances, it becomes imperative to develop robust algorithms that can adapt to evolving language and cultural contexts. By leveraging these technologies, we can promote inclusivity, protect vulnerable communities, and foster a more respectful and tolerant online space.

HATE SPEECH DETECTION AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI

by Vivian Siahaan (Kindle Edition)

4.6 out of 5

Language	:	English
File size	:	5502 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	221 pages
Lending	:	Enabled
Screen Reader	:	Supported

FREE

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, the objective is to predict the labels on the test dataset.

The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, LSTM, and CNN. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.