實驗結果表示在辨識惡意評論中，精準率(Precision)部分，TF-IDF搭配SVM分類器為本論文最佳組合；而召回率(Recall)部分，則以Word2vec搭配LSTM分類器為本論文最佳組合。 ;The development of the Internet has led to the rapid advancement of social media. Because the free speech and anonymity of social media characteristic, it causes abuse such as cyber harassment and Toxic Comments. Machine learning have changed many fields, for example computer vision, speech recognition and language processing. I will use the text classification of machine learning to effectively filter out Toxic Comments. The dataset is from the competition organized by Kaggle: Toxic Comment Classification Challenge, whose source is Wikipedia′s comments. These comments have been flagged as malicious and toxic by human evaluators. I will use Machine Learning (ML) method to match different Document representations for data analysis and prediction.
In this study, the Document representations of the text will use TF-IDF and Word2Vec for comparison and use KNN, SVM, ANN, Deep Learning as text classifier. This data set contains six multi-labels: toxic, severe_toxic, obscene, threat, insult, identity_hate, so the six labels are paired with different Document representations and text classifiers for comparative analysis.
The results show that in the Precision section, there is best predictive performance in TF-IDF combined with the SVM classifier than Word2Vec. About the Recall section, there is best predictive performance in Word2vec combined with LSTM classifiers.
