A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts (2022.acl-srw)
Copied to clipboard
| Challenge: | Sentiment analysis is a field that is growing due to the availability of the Internet and the growing number of online platforms. |
| Approach: | They propose an annotated Turkish dataset suitable for targeted sentiment analysis. |
| Outcome: | The proposed models outperform the traditional models for the targeted sentiment analysis task. |
Similar Papers
Multi-source Multi-domain Sentiment Analysis with BERT-based Models (2022.lrec-1)
Copied to clipboard
| Challenge: | Sentiment analysis is a widely studied task in natural language processing. |
| Approach: | They propose to improve BERT-based models for sentiment analysis on italian corpora and evaluate their performance on the basis of eight corpors. |
| Outcome: | The proposed model is evaluated over eight sentiment analysis corpora from different domains and sources on the prediction of positive, negative and neutral classes. |
Arabizi Language Models for Sentiment Analysis (2020.coling-main)
Copied to clipboard
| Challenge: | Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. |
| Approach: | They propose to use Arabizi as a written form of spoken Arabic in online social networks . they use a corpus of 7.7M tweets written in Arabizi and a subset of SALAD to train a model in Arabic . |
| Outcome: | The proposed model outperforms state-of-the-art models on sentiment analysis task using arabizi . the proposed model is based on a corpus of 7.7M tweets written in arabizi and a subset of LAD manually annotated for sentiment analysis. |
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency. |
| Approach: | They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities . |
| Outcome: | The proposed framework outperforms the sequence tagging baseline on three benchmark datasets. |
NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis (2022.lrec-1)
Copied to clipboard
Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Sa’id Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alípio Jorge, Pavel Brazdil
| Challenge: | Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. |
| Approach: | They propose a large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria. |
| Outcome: | The proposed dataset includes 30,000 tweets and a significant fraction of code-mixed tweets. |
A Turkish Hate Speech Dataset and Detection System (2022.lrec-1)
Copied to clipboard
| Challenge: | Davidson et al., 2017: hate speech is a discourse that targets a specific group based on race, gender, religion, sexual orientation, etc. |
| Approach: | They propose a machine learning system for automatic detection of hate speech in Turkish . they use a hate speech dataset and a dataset to collect tweets about immigrants . |
| Outcome: | The proposed system is able to detect hate speech in Turkish and annotate it using BERTurk. |
JL-Hate: An Annotated Dataset for Joint Learning of Hate Speech and Target Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing data resources for the detection of hate speech focus on text sequence classification, but the target of hateful content is lacking. |
| Approach: | They propose a tweet dataset for the task of joint learning of hate speech detection and target detection called JL-Hate. |
| Outcome: | The proposed dataset performs similar tasks to the existing datasets in sequence and token classification tasks. |
A Diverse Set of Freely Available Linguistic Resources for Turkish (2023.acl-long)
Copied to clipboard
| Challenge: | despite the abundance of Turkish speakers, linguistic resources for natural language processing remain scarce. |
| Approach: | They propose a set of freely available linguistic resources for Turkish natural language processing . they provide corpora and pretrained models to help practitioners build their own applications . |
| Outcome: | The proposed linguistic resources are first of their kind and easy to use in a broad range of implementations. |
XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection (2020.coling-main)
Copied to clipboard
| Challenge: | XED is a multilingual fine-grained emotion dataset for English and other low-resource languages. |
| Approach: | They propose a multilingual fine-grained emotion dataset using Plutchik's Wheel of Emotions and a projection scheme to annotate Finnish and English sentences. |
| Outcome: | The proposed dataset is based on human-annotated Finnish and English sentences and projected annotations for 30 additional languages. |
Author’s Sentiment Prediction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on inferring author sentiment in news articles hasn't been done on this domain. |
| Approach: | They propose a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. |
| Outcome: | The proposed dataset performs the best amongst the baselines, but only achieves modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. |
Cross-lingual Emotion Detection (2022.lrec-1)
Copied to clipboard
| Challenge: | Emotion detection is a useful tool for understanding human behavior, but constructing annotated datasets to train models can be expensive. |
| Approach: | They propose to use English as the source language with Arabic and Spanish as target languages to train models for emotion detection in a target language. |
| Outcome: | The proposed approaches surpass state-of-the-art models in Arabic and Spanish by 4% and 5% respectively. |