Papers with opinion mining
Investigating label suggestions for opinion mining in German Covid-19 social media (2021.acl-long)
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| Challenge: | Existing difficulties in data annotation are due to prolonged data gathering processes or opinion surveys being subject to reactivity. |
| Approach: | They propose to use label suggestions to improve annotation efficiency in german Covid-19 data by providing annotators with pre-recorded annotations. |
| Outcome: | The proposed model improves inter-annotator agreement and annotation quality in a controlled study with social science students. |
Opinion Mining with Deep Contextualized Embeddings (N19-3)
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| Challenge: | Existing methods for opinion expression detection are based on token-level sequence labeling . |
| Approach: | They propose to use BERT and conditional random field embedders to detect opinion expressions. |
| Outcome: | The proposed model outperforms ELMo embedders in opinion expression detection. |
Advances in Argument Mining (P19-4)
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| Challenge: | Argument mining is a rapidly growing area of research and research that has seen significant growth over the past few years. |
| Approach: | Argument mining is a new area of research that uses opinion mining to extract opinions . the 6th ACL workshop on argument mining will be in Florence in 2019 . |
| Outcome: | Argument mining is a new area of research and development that has seen significant growth in the past three years. |
Hybrid Models for Aspects Extraction without Labelled Dataset (D19-66)
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| Challenge: | Existing methods to extract aspects from opinions focus on explicit aspects, but sentences do not state them explicitly. |
| Approach: | They propose to use a dictionary-based approach to identify and extract aspects from opinions . they propose to combine topic modelling and dictionary--based method . |
| Outcome: | The proposed models outperform baseline topic model and dictionary-based approach in 58.70% of the evaluations. |
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (D19-1)
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| Challenge: | Existing methods to analyze emotions in textual conversations are limited . emotion detection is challenging because humans rely on context and commonsense knowledge to express emotions . |
| Approach: | They propose a Knowledge-Enriched Transformer where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged. |
| Outcome: | The proposed model outperforms state-of-the-art models on most of the tested datasets in F1 score. |
Mining Tweets that refer to TV programs with Deep Neural Networks (D19-55)
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| Challenge: | opinion mining is a popular natural language processing technique, but a problem is robustness for user-generated texts . a recent study shows that a model that handles context can extract the opinion target with 90% accuracy . |
| Approach: | They propose a model that handles context in many natural language processing areas to solve a problem of extracting opinion references from text. |
| Outcome: | Experiments on tweets that refer to television programs show the proposed model can extract opinion references with more than 90% accuracy. |
A Social Opinion Gold Standard for the Malta Government Budget 2018 (D19-55)
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| Challenge: | a dataset of opinionannotated social posts targeting the Malta Government Budget 2018 is presented . it contains opinions and reactions of the public and professionals regarding the budget as expressed over various social channels, including social networking services and newswires. |
| Approach: | They propose to annotate social opinions for the Malta Government Budget 2018 using over 500 online posts in English and/or the Maltese less-resourced language. |
| Outcome: | The proposed dataset contains over 500 opinionannotated social posts in English and/or the maltese less-resourced language, gathered from social media platforms. |
Arabizi Language Models for Sentiment Analysis (2020.coling-main)
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| 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. |
An Uncertainty-Aware Encoder for Aspect Detection (2021.findings-emnlp)
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| Challenge: | Existing methods for aspect detection use seed words as priors or features of topic models. |
| Approach: | They propose a weakly-supervised method to exploit seed words for aspect detection . goal is approximating similarity between segments and aspects and ground-truth similarity generated from seed words. |
| Outcome: | The proposed method outperforms previous work on several benchmarks in various domains. |
Task and Sentiment Adaptation for Appraisal Tagging (2023.eacl-main)
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| Challenge: | Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions. |
| Approach: | They propose to use language models to automatically identify and annotate text segments for appraisal. |
| Outcome: | The proposed model achieves superior performance than baseline adapter-based models and other neural classification models for cross-domain and cross-language settings. |
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining (2020.findings-emnlp)
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| Challenge: | Existing methods to classify and resolve coreferences in opinionated reviews require domain-specific knowledge. |
| Approach: | They propose to automatically mine domain-specific knowledge for opinionated reviews by combining it with commonsense knowledge. |
| Outcome: | The proposed approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage commonsense knowledge for the task. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
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| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction (2022.naacl-main)
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| Challenge: | Existing approaches to perform aspect and opinion co-extraction are difficult due to the lack of fine-grained annotations. |
| Approach: | They propose a framework to transfer knowledge from a labeled source domain to an unlabeled target domain. |
| Outcome: | The proposed framework is more effective than previous domain adaptation methods on three datasets. |
Comparative Opinion Quintuple Extraction from Product Reviews (2021.emnlp-main)
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| Challenge: | Comparative opinion mining is an important task in opinion mining. |
| Approach: | They propose a task to extract comparative opinion quintuples from product reviews . they propose supplementary annotations and construct three datasets for the task . |
| Outcome: | The proposed method outperforms baseline systems on three datasets and represents a strong benchmark for COQE. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020.coling-main)
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| Challenge: | Existing models for ERTC use a few non-neutral categories to identify the emotion of each utterance. |
| Approach: | They propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning to address these challenges by leveraging commonsense knowledge to leverage context. |
| Outcome: | The proposed model outperforms state-of-the-art models across five benchmark datasets. |
Seeds of Discourse: A Multilingual Corpus of Direct Quotations from African Media on Agricultural Biotechnologies (2025.findings-naacl)
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| Challenge: | a new study examines how media amplify messages around GM crops . agribusiness companies have placed ads for their products in newspapers . |
| Approach: | They present a multilingual corpora of 1,657 direct quotes from Africa-based news sources . they provide 665 instances annotated for Aspect-Based Sentiment Analysis . |
| Outcome: | The results of this study are available in English and French. |
CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts (2022.findings-emnlp)
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| Challenge: | Comparative opinion classification is a common linguistic phenomenon. |
| Approach: | They propose a framework for comparative opinion classification using embedded knowledge in pre-trained language models. |
| Outcome: | The proposed framework delivers state-of-the-art and robust performance on all benchmark datasets. |
Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration (2025.coling-main)
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| Challenge: | Rhetorical figures are used to convey subtle, implicit meanings or to emphasize statements. |
| Approach: | They propose a web application that facilitates the identification and annotation of German rhetorical figures. |
| Outcome: | The proposed application improves the user experience with Retrieval Augmented Generation (RAG). |
An Empirical Examination of Online Restaurant Reviews (2020.lrec-1)
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| Challenge: | Existing methods for opinion mining and sentiment analysis focus on extracting either positive or negative opinions from texts and determining the targets of these opinions. |
| Approach: | They propose a corpus-based scheme that detects evaluative language at a finer-grained level. |
| Outcome: | The proposed scheme classifies each sentence into one of four evaluation types based on the proposed scheme. |
Inference Annotation of a Chinese Corpus for Opinion Mining (2020.lrec-1)
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| Challenge: | Existing tools for opinion mining can accurately predict the writer's attitude in simple explicit sentences. |
| Approach: | They propose to define inference, classify different types and provide an annotation framework to analyze the annotation results. |
| Outcome: | The proposed framework defines inference type, polarity and topic and analyzes the results. |
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language (2020.lrec-1)
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| Challenge: | Aspect Based Sentiment Analysis (ABSA) is a finer level sentiment analysis that assigns polarity to each targeted aspect instead of the entire review. |
| Approach: | They propose to use Telugu as a language for aspect based sentiment analysis . they use a resource that can be used to classify and categorise aspects of a review . |
| Outcome: | The proposed resource is based on a set of tasks in Telugu which demonstrate its reliability and usefulness. |
Annotating Attribution Relations in Arabic (L18-1)
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| Challenge: | Current studies focus on using lexical terms in long texts to verify author identity. |
| Approach: | They propose to annotate attributed arguments to the source in Arabic news with required syntactical and semantic features with required features. |
| Outcome: | The proposed method is applied to Arabic news and is compared with existing tools and methods. |
NoReC: The Norwegian Review Corpus (L18-1)
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Erik Velldal, Lilja Øvrelid, Eivind Alexander Bergem, Cathrine Stadsnes, Samia Touileb, Fredrik Jørgensen
| Challenge: | The Norwegian Review Corpus is a dataset of full-text reviews from major news sources. |
| Approach: | This paper presents the Norwegian Review Corpus, created for document-level sentiment analysis. |
| Outcome: | The corpus comprises more than 35,000 full-text reviews from a range of different domains. |
SenSALDO: Creating a Sentiment Lexicon for Swedish (L18-1)
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| Challenge: | sentiment analysis has seen an explosive expansion over the last decade or so . many theoretical and methodological questions remain unanswered and resource gaps unfilled . |
| Approach: | They develop a sentiment lexicon for written (standard) Swedish using an existing dataset . they assign a real value sentiment score in the range [-1,1] and produce a label for it . |
| Outcome: | The proposed sentiment lexicon is an open source resource from the Swedish Language Bank . it is based on an existing gold standard dataset and is available from Sprkbanken . |
A deep-learning framework to detect sarcasm targets (D19-1)
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| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |
GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification (2025.coling-main)
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| Challenge: | Existing models generate text on demand, but in real-life situations, individuals do not continuously generate text or voice opinions. |
| Approach: | They propose a novel task to identify news-triggered opinion expressing timing by using a dataset generated by professional stock analysts. |
| Outcome: | The proposed model can generate opinion on stock analysts' actions and improves performance in various opinion understanding tasks. |
EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)
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| Challenge: | Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV). |
| Approach: | They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing. |
| Outcome: | The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously. |
Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States (2024.lrec-main)
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Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, SeyedAbolghasem Mirroshandel, Owen Rambow
| Challenge: | Existing research on opinion mining has focused on a small subset of the MPQA 2.0 dataset . a recent study focused on the subjective expressions of people who express opinions, sentiments, and attitudes toward targets. |
| Approach: | They propose to use MPQA 2.0 to analyze the entire dataset . they propose to provide a clean version of the MPQA Opinion Corpus in a more interpretable format . |
| Outcome: | The proposed methods establish high baselines for future work. |
I love pineapple on pizza != I hate pineapple on pizza: Stance-Aware Sentence Transformers for Opinion Mining (2024.emnlp-main)
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| Challenge: | Sentence transformers excel at grouping topically similar texts, but struggle to differentiate opposing viewpoints on the same topic. |
| Approach: | They propose to fine-tune sentence transformers with arguments for and against controversial claims to enhance their utility for social computing tasks. |
| Outcome: | The proposed model improves opinion mining and stance detection tasks by combining human-generated controversial claims with stance-aware sentences. |