Enhancing Low-Resource Sentiment Analysis: A Transfer Learning Approach

Abstract

The identification and extraction of subjective information from text, known as sentiment analysis, has seen advancements in employing cross-lingual approaches. However, the effective implementation and evaluation of sentiment analysis systems necessitate languagespecific data to account for diverse sociocultural and linguistic variations. This paper outlines the process of collecting and annotating a dataset for sentiment analysis in Central Kurdish. We investigate classical machine learning and neural network-based techniques for this purpose. Furthermore, we adopt a transfer learning approach to enhance performance by leveraging pre-trained models for data augmentation. Our results demonstrate that despite the challenging nature of the task, data augmentation contributes to achieving high F1 scores and accuracy.

A Linguistic Evaluation of Google Translate Between Human and Machine Translation for English-Sorani Kurdish Languages Using Natural Language Processing (NLP)

“…The Recall, Precision, and F1 scores for the reference 𝑥 and candidate 𝑥 ̂ are: Where RBERT counts the number of correctly translated words compared to the machine-translated words, PBERT counts the number of candidate translation words (unigrams) that occur in any reference translation by the total number of words in the candidate translation. The F-measure (FBERT) of the translation is equal to the multiplication of the Precision and recall divided by the addition of the Precision and Recall [21], [27][28].…”