Deep Learning Model for Sentiment Analysis on Short Informal Texts

Abstract

This paper proposes a classification model to classify short informal texts. Those short informal texts were texts that were noisy, typos, irregular, and could consist of a very small number of words or even only a single word. The proposed model was trained using a dataset collected from student comments from an application called Evaluasi Dosen Oleh Mahasiswa (EDOM). This application assesses the lecturers using questionnaires filled out by students. It also records the student's comments but is not part of the evaluation calculation, therefore this work makes the data possible to be part of the assessment through sentiment analysis. This work focuses on building suitable preprocessing algorithm and building a simple deep learning network. The preprocessing algorithm was based on multiple word n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, and the network was built with a relatively shallow network. To evaluate the model in real usage, an application was built. The results were very convincing, reaching 0.979 in accuracy and 0.63 in F1-Score. Nonetheless, the imbalanced dataset was the only factor that needed to be investigated further for better overall performance.