Semi-supervised Learning Models for Sentiment Analysis on Marketplace Dataset

Abstract

Sentiment analysis aims to categorize opinions using an annotated corpus to train the model. However, building a high-quality, fully annotated corpus takes a lot of effort, time, and expense. The semi-supervised learning technique efficiently adds training data automatically from unlabeled data. The labeling process, which requires human expertise and requires time, can be helped by an SSL approach. This study aims to develop an SSL-Model for sentiment analysis and to compare the learning capabilities of Naive Bayes (NB) and Random Forest (RF) in the SSL. Our model attempts to annotate opinion documents in Indonesian. We use an ensemble multi-classifier that works on unigrams, bigrams, and trigrams vectors. Our model test uses a marketplace dataset containing rating comments scrapping from Shopee for smartphone products in the Indonesian Language. The research started with data preparation, vectorization using TF-IDF, feature extraction, modeling using Random Forest (RF) and Naïve Bayes (NB), and evaluation using Accuracy and F1-score. The performance of the NB model outperformed previous research, increasing by 5,5%. The conclusion is that SSL performance highly depends on the number of training data and the compatibility of the features or patterns in the document with machine learning. On our marketplace dataset, better to use Random Forest.

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

“…Moreover, the sentiments of people towards Moroccan universities were assessed using RF, MNB, LR DT, SVM, and XGboost, as well as the results indicated that the highest accuracy at 98% [20]. Another sentiment analysis was also conducted on a marketplace dataset using naï ve Bayes and random forest and the highest accuracy was achieved by Naï ve Bayes at 87% [21]. Nine algorithms were also analyzed using five datasets and the most accurate result of 85.1% was recorded in Naï ve Bayes, RF, and LR [22].…”