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Determining Hateful and Offensive Terms from Twitter Using Hate Speech Detection
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| Author:
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S. SANTHI, N. UMAKANTH, T. HEMALATHA
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| Abstract:
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Social media has become a powerful tool for exchanging information about various techniques, ideas through
twitter, Facebook, Instagram, blogs, etc., sometimes communication between different people among different
cultural, background which will turn into serious conversation. Such websites (services) offer an open space
for people to discuss and share thoughts and opinion with huge number of posts, comments and messages.
This may lead to more aggressive speech in social network. Hate is sentiment which also known in the form of
negative tweets and hate mostly focus on words and expressions the person uses. The dataset is classified into
three classes such as clean, hate, offensive. To overcome such issue, sentiment analysis is used for detecting
hate speech and offensive languages from the twitter data through various processes. And also, to detect hate
speech from twitter, we are using supervised machine learning classifier such as Support Vector Machine,
Naïve Bayes and K Nearest Neighbour. The classification is managed through feature extraction and also
compares the accuracy for different classifier.
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| Keyword:
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Sentiment Analysis, Natural Language Processing, Machine Learning Techniques, Feature Extraction, Hate Speech Detection.
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| EOI:
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| DOI:
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| Download:
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