Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency

Sentiment analysis can detect hate speech using the Natural Language Processing (NLP) concept. This process requires annotation of the text in the labeling. However, when carried out by people, this process must use experts in the field of hate speech, so there is no subjectivity. In addition, if processed by humans, it will take a long time and allow errors in the annotation process for extensive data. To solve this problem, we propose an automatic annotation process with the concept of semi-supervised learning using the K-Nearest Neighbor algorithm. This process requires feature extraction of term frequency-inverse document frequency (TF-IDF) to obtain optimal results. KNN and TF-IDF were able to annotate and increase the accuracy of < 2% from the initial iteration of 57.25% to 59.68% in detecting hate speech. This process can annotate the initial dataset of 13169 with the distribution of 80:20 of training and testing data. There are 2370 labeled datasets; for testing, there are 1317 unannotated data; after preprocessing, there are 9482. The final results of the KNN and TF-IDF annotation processes have a length of 11235 for annotated data.

@article{Cahyana2022,
  title     = {Semi-supervised Text Annotation for Hate Speech Detection using K-Nearest Neighbors and Term Frequency-Inverse Document Frequency},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {10},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Nur Heri Cahyana and Shoffan Saifullah and Yuli Fauziah and Agus Sasmito Aribowo and Rafal Drezewski},
  doi       = {10.14569/IJACSA.2022.0131020},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131020}
}