Transfer Learning for Low-Resource Sentiment Analysis

• URL: http://arxiv.org/abs/2304.04703v1
• Date: Mon, 10 Apr 2023 16:44:44 GMT
• Title: Transfer Learning for Low-Resource Sentiment Analysis
• Authors: Razhan Hameed and Sina Ahmadi and Fatemeh Daneshfar
• Abstract summary: In this paper, the collection and annotation of a dataset are described for sentiment analysis of Central Kurdish. We explore a few classical machine learning and neural network-based techniques for this task.
• Score: 1.2891210250935146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentiment analysis is the process of identifying and extracting subjective information from text. Despite the advances to employ cross-lingual approaches in an automatic way, the implementation and evaluation of sentiment analysis systems require language-specific data to consider various sociocultural and linguistic peculiarities. In this paper, the collection and annotation of a dataset are described for sentiment analysis of Central Kurdish. We explore a few classical machine learning and neural network-based techniques for this task. Additionally, we employ an approach in transfer learning to leverage pretrained models for data augmentation. We demonstrate that data augmentation achieves a high F$_1$ score and accuracy despite the difficulty of the task.

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