# A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts

arXiv: https://arxiv.org/abs/2205.04185 · HTML: https://mlsanity.com/paper/2205.04185/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Interactive Attention Networks for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/1709.00893/) (2017)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015)
- [Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1910.00883/) (2019)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic](https://mlsanity.com/paper/2008.12172/) (2020)
- [Targeted Sentiment Analysis: A Data-Driven Categorization](https://mlsanity.com/paper/1905.03423/) (2019)

## Built on by (most cited first, 7 of 7)

- [LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma](https://mlsanity.com/paper/2403.00418/) (2024)
- [M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets](https://mlsanity.com/paper/2404.01753/) (2024)
- [Turkish Delights: a Dataset on Turkish Euphemisms](https://mlsanity.com/paper/2407.13040/) (2024)
- [Linguistic features for sentence difficulty prediction in ABSA](https://mlsanity.com/paper/2402.03163/) (2024)
- [A Cross-Validation Study of Turkish Sentiment Analysis Datasets and Tools](https://mlsanity.com/paper/2412.05964/) (2024)
- [Unravelling Technical debt topics through Time, Programming Languages and Repository](https://mlsanity.com/paper/2504.11714/) (2025)
- [Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model](https://mlsanity.com/paper/2606.29614/) (2026)

## Abstract (arXiv)

Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to generate an enormous amount of data. Sentiment analysis, which in general requires annotated data for training, is a well-researched area for widely studied languages such as English. For low-resource languages such as Turkish, there is a lack of such annotated data. We present an annotated Turkish dataset suitable for targeted sentiment analysis. We also propose BERT-based models with different architectures to accomplish the task of targeted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.