Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates (N18-1)
Copied to clipboard
| Challenge: | Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future and affects personality, motivation, emotion, decision making and stress coping processes. |
| Approach: | They propose to use a minimally supervised method to classify tweets in one of three temporal categories, past, present, and future, and a deep bi-directional long-term memory (BLSTM) to measure correlation between sentiment view of temporal orientation and different psycho-demographic factors. |
| Outcome: | The proposed method achieves 78.27% accuracy on a manually created test set. |
Similar Papers
Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for financial sentiment analysis use random splits of a dataset into training and testing to ensure there is no distribution shift between training and deployment. |
| Approach: | They propose a method that combines out-of-distribution detection with time series modeling for temporal financial sentiment analysis. |
| Outcome: | The proposed method improves the model’s ability to adapt to evolving temporal shifts in a volatile financial market. |
Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing models that model fine-grained opinion shifts of social media users are lacking . lack of publicly available datasets for this task presents a major challenge . |
| Approach: | They propose an annotated social media opinion dataset that provides a model for subtle opinion fluctuations and fine-grained stances. |
| Outcome: | The proposed dataset is comparable to the annotations of experts and non-experts. |
Fine-Grained Temporal Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that predict stock movements are based on time series and technical analysis, but price signals alone fail to capture market surprises and impacts of sudden unexpected events. |
| Approach: | They propose a model that integrates chaotic temporal signals from financial data and social media to create hierarchical temporal networks. |
| Outcome: | The proposed model can be used to forecast stock movements on real-world S&P 500 index data and English tweets. |
Evaluating Short-Term Temporal Fluctuations of Social Biases in Social Media Data and Masked Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Social biases such as gender or racial biase are reported in language models . a recent study has shown that MLMs encode discriminatory social biase . |
| Approach: | They analyse temporal corpora of MLMs trained on chronologically ordered temporal snapshots . they find that gender and racial biases are encoded in MLM models . |
| Outcome: | The proposed model identifies gender biases in MLMs but most remain stable over time . gender bias is associated with higher likelihood scores in some demographic groups . |
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for temporal knowledge graphs can hardly model temporal relation patterns, lacking of interpretability. |
| Approach: | They propose a temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space and relations as complex vectors in Hamilton’s quaterniont space. |
| Outcome: | The proposed method can model key patterns of relations in TKG, such as symmetry, asymmetry, and inverse, and can capture time-evolved relations by theory. |
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users. |
| Approach: | They propose a multi-perspective role-playing framework to simulate human response processes to extract sentiment-related features from social media messages. |
| Outcome: | The proposed model improves sentiment forecasting at microscopic and macroscopic levels. |
Temporal reasoning for timeline summarisation in social media (2025.acl-long)
Copied to clipboard
| Challenge: | Existing temporal reasoning datasets focus on pair-wise event relationships. |
| Approach: | They propose a temporal reasoning dataset focused on temporal relationships among sequential events within narratives that combines temporal thinking with timeline summarisation through a knowledge distillation framework. |
| Outcome: | The proposed model achieves superior performance on mental health-related timeline summarisation tasks, highlighting the importance and generalisability of leveraging temporal reasoning to improve timeline summaries. |
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies have used a manual segmentation of a tweet sequence into equallyspaced intervals based on either tweet counts or time duration. |
| Approach: | They propose to model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. |
| Outcome: | The proposed model predicts the posting time and the stance labels of future tweets more accurately compared to baselines. |
Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2022.naacl-main)
Copied to clipboard
| Challenge: | a pretrained model is optionally adapted through domain-specific pretraining, followed by task-specific finetuning. |
| Approach: | They establish a suite of eight tasks across different domains to quantify the effects of temporal misalignment in modern NLP systems. |
| Outcome: | The proposed tasks are based on eight domains and periods of time spanning five years or more and show that they have stronger effects than previous studies. |