Open AccessDetecting and Classifying Toxic Language in Twitter using Machine Learning
Author(s) -
Nischal Lakhotia,
Omprakash Harod,
T. Manoranjitham
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9714.069520
Subject(s) - offensive , tf–idf , computer science , artificial intelligence , feature (linguistics) , natural language processing , machine learning , feature extraction , speech recognition , engineering , linguistics , philosophy , physics , quantum mechanics , operations research , term (time)
Today international on-line content material has turned out to be a first-rate part due to growth in the use of net. Individuals of various societies and instructive foundation can speak through this platform. Therefore, for automatic detection of poisonous content, we need to distinguish between hate speech and offensive language. Here a way to robotically stumble on and classify tweets on Twitter into 3 commands: hateful, offensive and easy is proposed. We do not forget n-grams as functions and by way of passing their time period frequency-inverse document frequency (TFIDF) values to numerous system gaining knowledge of fashions using Twitter dataset, we perform comparative evaluation of the models. We work towards classification and comparison of different classifiers using the combination of best feature from each type of feature extraction and determining which model works best for the purpose of classification of tweets into hate-speech, offensive language or neither.