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Updated: Jun 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A Kurdish Sorani Twitter dataset for language modelling.

Shakhawan Hares Wady¹, Soran Badawi², Fatih Kurt³

¹Department of Business Administration, Charmo University, KRG, Chamchamal, Kurdistan, Iraq.

|October 22, 2024

Summary

This summary is machine-generated.

This study introduces a new Kurdish Twitter dataset for sentiment analysis. The resource aids researchers in developing advanced natural language processing models for the Kurdish language.

Keywords:

Data mining Low-resource languages Natural language processing Preprocessing

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Area of Science:

Natural Language Processing
Computational Linguistics
Social Media Analysis

Background:

Sentiment analysis is crucial for understanding opinions, but Kurdish language resources are scarce.
Existing sentiment analysis datasets are limited for low-resource languages like Kurdish.

Purpose of the Study:

To create a comprehensive and robust sentiment analysis dataset for the Kurdish language.
To facilitate research and development of sentiment analysis models for Kurdish social media text.

Main Methods:

Collected 24,668 annotated tweets from Twitter in Kurdish.
Annotators labeled tweets for subjectivity, sentiment (positive, negative, neutral), offensiveness, and target.
Ensured data quality through independent review and cleaning.

Main Results:

The dataset contains 8,772 subjective and 15,896 non-subjective tweets.
Sentiment distribution: 12,938 negative, 3,189 neutral, 8,541 positive.
Classified 2,232 offensive and 22,436 non-offensive tweets, with 2,232 targeted tweets.

Conclusions:

This curated dataset is a valuable resource for advancing Kurdish sentiment analysis.
Enables the development of sophisticated NLP models for Kurdish language understanding.
Addresses a critical gap in NLP resources for the Kurdish language.