Enhancing text classification performance by preprocessing misspelled words in Indonesian language
Reza Setiabudi, Ni Made Satvika Iswari, Andre Rusli
Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments.
Indonesian language; levenshtein distance; text classification; typo correction; user feedback;