A Novel Way of Identifying Cyber Predators
This work addresses the critical issue of online safety by improving detection of sexual predators, though it appears incremental as it builds on existing LSTM-RNN and sentiment analysis methods.
The research tackled the problem of identifying sexual predators in online conversations by combining sentiment analysis, a new sentence vector approach, and LSTM-RNN, achieving a record-breaking accuracy and precision of 100% with a recall of 81.10%.
Recurrent Neural Networks with Long Short-Term Memory cell (LSTM-RNN) have impressive ability in sequence data processing, particularly for language model building and text classification. This research proposes the combination of sentiment analysis, new approach of sentence vectors and LSTM-RNN as a novel way for Sexual Predator Identification (SPI). LSTM-RNN language model is applied to generate sentence vectors which are the last hidden states in the language model. Sentence vectors are fed into another LSTM-RNN classifier, so as to capture suspicious conversations. Hidden state enables to generate vectors for sentences never seen before. Fasttext is used to filter the contents of conversations and generate a sentiment score so as to identify potential predators. The experiment achieves a record-breaking accuracy and precision of 100% with recall of 81.10%, exceeding the top-ranked result in the SPI competition.