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The vast growth in technology and social media has revolutionized our lives by transforming the way we connect and communicate. Unfortunately, the darker side of this development has exposed a lot of children and teenagers from various ages to become victims of online sexual abuse.

To help combat the severity of the problem, I ed an Omdena project together with the Zero Abuse Project.

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Among 45 Omdena collaborators from across 6 continents, the goal was to build AI models to identify patterns in the behavior of institutions when they cover-up incidents of sexual abuse. The identification and analysis of sexual crimes assure public safety and has been made possible by leveraging AI.

Natural Language Processing and various machine learning techniques have played a major in the successful identification of online sexual abuse. The main idea of this task was to classify online chats between two persons as sexual abuse or non-sexual abuse. In the following example, our idea aimed at classifying the chats as predatory or non-predatory.

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Classifying online chats. However, the realistic data provided by PAN has a high noise level with unbalanced training samples and varying length of conversations.

How to improve your next long-distance sex session environments[ edit ] cybersex is commonly performed in internet chat rooms such as irc , talkers or web chats and on instant messaging systems.

Such words are necessary cybersex chat text feature selection and for improving the performance of the model used for the classification. Wait, are we stuck with preprocessing?

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Initially, with the huge dataset and high noise levels, preprocessing did seem like a herculean task! We managed to implement it by using text mining techniques. We started off by carrying out a basic analysis of checking for null characters, finding the sentence length of each text message as well as finding out the words with the highest frequencies.

We also implemented the removal of stopwords, stemming, and lemmatization.

Introduction

The aim of both stemming and lemmatization is to reduce the corpus size and complexity for creating embeddings from simpler words which is useful for sentiment analysis. Furthermore, we realized our dataset contained lo of emojis, URLs, hashtags, misspelled words, and slangs. In order to reduce the noise levels to a greater extent, we had to remove the emoticons from the chats using regular expressions and change the misspelled words by creating a dictionary.

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The tricky part here involved converting the chat slang abbreviations since it was necessary for feature selection. Unfortunately, it was difficult to find a library or database of words that do that. We had to create a dictionary for that purpose.

The Exploratory Data Analysis.

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We further tried to analyze the top 20 frequently words in the chatlogs as unigrams and bigrams. A unigram is an n-gram consisting of a single word from a sequence and bigrams contain two words from a sequence.

Moving into the language model and classification. The XML dataset provided cybersex chat text PAN is unlabelled and manual labeling is a pretty difficult task considering the of samples present in the dataset.

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To solve this situation, sentiment analysis was carried out to identify the polarity and subjectivity of the chatlogs. Polarity is a float which lies in the range of [-1,1] where 1 means positive statement and -1 means a negative statement. Subjective sentences generally refer to personal opinion, emotion, or judgment whereas objective refers to factual information.

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Subjectivity is also a float which lies in the cybersex chat text of [0,1]. Considering the different of sentences in conversations from 1 to more thanthe extra-long conversations were padded by zeros and then split into parts, each with an equal length of GloVe stands for global vectors for word representation.

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It is an unsupervised learning algorithm developed by Stanford for generating word embeddings by aggregating a global word-word co-occurrence matrix from a corpus. It consists of one embedding layer, two LSTM-RNN layers with units and 50 timesteps as well as a sigmoid layer that is implemented on the Tensorflow framework for the binary classification. The could have been improved if labeling the chatlogs could be efficient and if the persisting noise in the dataset could be reduced.

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However, this task of classifying the sexual predators provided us a clearer perspective of an efficient feature selection and new approaches to solving the labeling problem in order to improve the accuracy of the LSTM-RNN classification. Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

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Save my name,and website in this browser for the next time I comment. Be notified a few times a month about top-notch articles, new real-world projects, and events with our community of changemakers. Classifying the online chats between two persons as sexual abuse or non-sexual abuse using text mining and deep learning. Top 20 Unigrams.

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