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Detect Toxic Content to Improve Online Conversations

2019-10-29 · Deepshi Mediratta, Nikhil Oswal

Social media is filled with toxic content. The aim of this paper is to build a model that can detect insincere questions. We use the 'Quora Insincere Questions Classification' dataset for our analysis. The dataset is composed of sincere and insincere questions, with the majority of sincere questions. The dataset is processed and analyzed using Python and its libraries such as sklearn, numpy, pandas, keras etc. The dataset is converted to vector form using word embeddings such as GloVe, Wiki-news and TF-IDF. The imbalance in the dataset is handled by resampling techniques. We train and compare various machine learning and deep learning models to come up with the best results. Models discussed include SVM, Naive Bayes, GRU and LSTM.

📄 PDF Abstract BibTeX arXiv:1911.01217

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Word Embeddings

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음
Tanh Activation 설명 없음
SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…
GloVe GloVe Embeddings are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares…
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…
GRU A Gated Recurrent Unit, or GRU, is a type of recurrent neural network. It is similar to an LSTM, but only has two gates - a reset…

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