Study of Machine learning algorithm to detect hate speech to help prevent harmful effects.
Machine learning algorithms can be used to detect hate speech. These algorithms can analyze text and identify hate speech. They can also be used to determine the tone of a text. This can be used to identify hate speech that is disguised as jokes or sarcasm.
Machine leaning is a type of artificial intelligence that can be used to learn from data. It can be used to find patterns in data. Machine learning algorithms can be used to detect the speech. These algorithms can analyze text and identify hate speech. They can also used to determine the tone of a text. Automated hate speech detection is an important tool in combating the spread of hate speech in social media. The techniques for detecting hate speech suing machine learning include classifiers, deep learning. It can be used to find patterns in data. Natural Language processing techniques can be used to detect hate speech.
Datasets – any form of dataset is required to analyze the data without data no evaluation can be done thus no result will be there.
Dictionaries – content words and n-grams
Bag of words
N-grams: word and character
Word embeddings: Distribution of bag of words.
Tools such as word2vec, glove, fastext
TF-IDF, Part-of-speech, NER, dependency parsing
NumPy, pandas, matplotlib, pyplot, Wordcloud, sklearn.
nltk(natural language toolkit) re (regular expression)