Detects non-linear causality between social media sentiment and cryptocurrency prices.
problem Detecting causality between non-linear time series data.
method Use of transfer entropy to measure information transfer, validating against synthetic data, and applying significance tests.
result Significant non-linear causality detected, orders of magnitude greater than linear causality.
BERT improves fine-grained sentiment classification.
problem Fine-grained sentiment classification of text.
method Used BERT for fine-grained sentiment classification.
result BERT outperforms other models for fine-grained sentiment classification.
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
FinEAS models financial sentiment using BERT embeddings.
problem Financial sentiment analysis in markets.
method Supervised fine-tuning of BERT embeddings for financial texts.
result FinEAS outperforms vanilla BERT, LSTM, and FinBERT.
A new neural network model for sentiment analysis.
problem Accurate and transferable sentiment analysis models.
method A computationally-efficient feedforward neural network.
result Highly accurate models with low losses.
Transfer learning improves sentiment classification using XR framework.
problem Lack of labeled data for deep learning.
method XR framework applied to transfer learning between related tasks, using expected label proportions.
result Improved performance on aspect-based sentiment classification.
Improved sentiment analysis in Korean finance using masked PLMs.
problem Fine-grained sentiment analysis in non-English finance literature is lacking due to limited annotated data.
method Developed KorFinASC dataset and applied TGT-Masking to PLMs to remove non-stationary knowledge.
result Improved classification accuracy by 22.63% on KorFinASC.
Paper tackles ASC with coarse-to-fine task transfer for AT-level sentiment classification.
problem Small AT-level corpora and limited data for fine-grained AT task.
method Coarse-to-fine task transfer using MGAN with Coarse2Fine attention and contrastive feature alignment.
result MGAN improves ASC performance on AT-level sentiment classification.
Preserves content while changing style in text.
problem Text style transfer often fails to preserve content.
method Leverages linguistic information in structured supervisions.
result Significant improvement in content preservation and style transfer.
Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event. Although researchers have investigated several methods to reliably infer sentiment from lexical, speech and body language cues, training a model with a small set of labeled datasets is still a challeng…
Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propo…
This paper compares deep transfer learning with classical ML in low-shot text classification.
problem Low-shot text classification with limited labeled data.
method Comparison of BERT and top classical ML approaches on a sentiment classification task.
result BERT outperforms classical ML by 9.7% on average with 100 labeled examples per class.
Method selects task-specific neurons for unsupervised transfer learning.
problem Challenges in fine-tuning large language models for specific tasks.
method Selects important neurons for a specific classification task and extends to multi-source transfer learning.
result Higher similarity between task-specific fingerprints leads to better transferability.
Paper tackles domain invariant sentiment classification using weak supervision.
problem Learning a sentiment classification model that adapts to any target domain.
method Two-stage training procedure with weakly supervised datasets.
result Transfer learning with weak supervision achieves performance close to supervised training.
A new method improves cross-domain sentiment analysis by learning weighted domain-invariant representations.
problem Label distribution changes across domains harm domain adaptation in DIRL.
method Proposes WDIRL, a modification to DIRL that learns weighted domain-invariant representations.
result Empirical studies show the effectiveness of WDIRL in cross-domain sentiment analysis.
Study shows publicly available news impacts financial markets.
problem Impact of publicly available news on financial markets.
method Extracted news from Common Crawl, identified relevant companies, used sentiment analysis and information theory.
result Publicly available news has significant impact on financial markets.
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
problem Real-time price prediction for high-volatility cryptocurrencies.
method Spark-based architecture, sentiment analysis, online learning.
result Real-time adaptation of learning algorithms to new data.
Generative model evaluates text emotion intensity, outperforming classification.
problem Limitations of discrete emotion classification in applied domains.
method Fine-tuning generative language models to output continuous emotion intensity scores.
result Generative model outperforms classification baselines and reveals generalization capabilities.
Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more stru…
Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e…
AlphaMLDigger predicts excess returns in fluctuating markets.
problem Mining effective information for investment decisions in a volatile market.
method Two-phase approach using deep NLP for sentiment analysis and ensemble ML models.
result Ensemble models achieve 0.984 accuracy, significantly outperforming baseline.
This paper improves sentence-level sentiment analysis of financial news.
problem Blurred insights into individual sentences' sentiment in financial news.
method Distributed text representations and multi-instance learning.
result Superior predictive performance of up to 69.90%.
New method transfers word embeddings from large to small datasets efficiently.
problem Challenges in learning word embeddings from new domains with limited data.
method Group-sparse matrix factorization for transfer learning.
result Efficiently learns domain-specific word embeddings with less data.
GIRNet tackles multi-tasking with mixed domain sequences, improving sentiment and tagging tasks.
problem Labeling sequences with mixed domain data and position-specific inference.
method Unified position-sensitive multi-task RNN architecture with gated state sequences from auxiliary data.
result GIRNet achieves new state-of-the-art performance in sentiment classification, POS tagging, and target position-sensitive annotation.
Paper introduces metrics to assess and control nuisance factors in sentiment analysis.
problem Challenges in learning invariant representations for sentiment analysis due to entangled nuisance factors.
method Developed two generalization metrics and a data filtering approach to control nuisance factors.
result Simple text classification baseline can be badly affected by product ID in sentiment analysis.
Paper proposes DDA for better transfer learning performance.
problem Domain discrepancy between source and target distributions.
method Dynamic Distribution Adaptation (DDA) to evaluate and adapt distribution importance.
result DDA improves transfer learning performance on various tasks.
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence cl…
Quantformer uses transformer to predict stock returns, outperforming traditional strategies.
problem Predicting stock returns in a dynamic financial market.
method Transfer learning from sentiment analysis to build investment factors using a transformer-based neural network.
result Quantformer outperforms other 100-factor-based quantitative strategies in predicting stock trends.
Survey of LLMs in finance tasks, highlighting progress and challenges.
problem Transforming financial practices with advanced LLMs.
method Exploration of various financial tasks, categorization, and analysis of methodologies.
result Unlocking novel opportunities for financial applications with LLMs.
Study finds investor sentiment has a significant positive relationship with stock returns in Moroccan and Tunisian markets.
problem Investor sentiment and stock returns relationship in Moroccan and Tunisian markets.
method Used indirect measures of investor sentiment (SENT and ARMS) and Granger causality tests.
result Sentiment has a significant positive relationship with stock returns, but not the other way around.
A new teacher-class network method compresses DNNs by distributing knowledge to multiple student networks.
problem Overwhelming size of Deep Neural Networks (DNNs).
method Single teacher with multiple student networks, transferring knowledge to each student.
result The combined knowledge of the class of students achieves better performance and reduces parameters.
News sentiment in U.S. economic newspapers has become more persistent over 45 years.
problem Understanding the temporal dynamics of U.S. economic news sentiment over time.
method Daily economic news sentiment index from 1980-2025, analyzed using sentiment indexes.
result News sentiment states have become more persistent, with longer residence times in optimistic or pessimistic regimes.
LLMs improve financial sentiment analysis in finance.
problem Defining and measuring financial sentiment.
method Investigation of sentiment measurement methods and LLMs.
result LLMs enhance financial sentiment analysis.
Study uses Granger causality to show investor sentiment influences stock prices.
problem Understanding the relationship between investor sentiment and stock market movements.
method Applied Granger causality to analyze the relationship between close price index and sentiment score.
result Sentiment analysis shows a positive correlation with stock price movements.
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification…
Method quantifies sentiment across languages without labeled data.
problem Performing sentiment analysis across multiple languages without labeled data.
method Combining quantification methods with cross-lingual vectorial representations.
result Cross-lingual sentiment quantification achieved surprising accuracy.
Investor sentiment improves model accuracy but complexity doesn't always boost predictive power.
problem Determining the optimal complexity of investor sentiment measures in asset pricing models.
method Comprehensive review of 71 papers from 2000-2021, analyzing various sentiment measures and models.
result Higher complexity of sentiment measures does not necessarily improve predictive power.
The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, however, not one which a computer can perform by default. Identifying sentiments in an automated, algorithmic manner will be a useful capability for business and research in their search …
Model quantifies market sentiment using news data.
problem Quantifying high-frequency market sentiment for economists.
method Support vector machine classifiers for sentiment analysis; stochastic volatility model for joint evolution.
result News sentiment raises the threshold of volatility reversion.
TSPRA integrates topics, sentiment, and user preference for better online review prediction and analysis.
problem Improving online review prediction and sentiment analysis accuracy.
method HDP-based model combining topics, sentiment, and user preference.
result Outperforms state-of-the-art model FLAME in rating prediction and sentiment analysis.
Enhanced portfolio selection using sentiment data and LSTM.
problem Improving portfolio selection through sentiment analysis and price prediction.
method Semantic Attention Model for sentiment prediction, LSTM for price prediction, mean-variance strategy for portfolio optimization.
result Sentiment-aware portfolio strategies outperform non-sentiment aware models on average.
Dual-decoder model generates responses with targeted sentiment.
problem Generating human-like responses with specific sentiment.
method Simple dual-decoder model with two sentiment decoders connected to one encoder.
result Significant performance gain in sentiment accuracy and word diversity.
Sentiment analysis of DAX40 stocks improves performance by 5.38% annually.
problem Creating a more responsive stock market index using sentiment analysis.
method Extract sentiment from news articles, adjust index weights based on sentiment, compare performance to existing indices.
result Sentiment index outperforms DAX40 by 7.51% annually, adjusted for costs.
Study enhances cryptocurrency sentiment analysis using TikTok and Twitter data.
problem Lack of comprehensive sentiment analysis in cryptocurrency markets.
method Multimodal analysis of TikTok and Twitter data using large language models.
result TikTok's video sentiment influences speculative assets and short-term trends.
In this paper, we design an integrated algorithm to evaluate the sentiment of Chinese market. Firstly, with the help of the web browser automation, we crawl a lot of news and comments from several influential financial websites automatically. Secondly, we use techniques of Natural Language Processing(NLP) under Chinese…
Study uses sentiment analysis to predict implied volatility surface, improving prediction accuracy.
problem Improving prediction accuracy of implied volatility surface.
method Constructed daily high-frequency sentiment data, used VAR method, deep learning (BERT, LSTM), FFT, EMD for sentiment decomposition.
result High-frequency sentiment correlates with ATM options' implied volatility, low-frequency with DOTM options.
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop…
ArSentD-LEV dataset improves sentiment analysis in Levantine Arabic tweets.
problem Challenges in sentiment analysis of Arabic tweets, especially Levantine dialect.
method Created a dataset of 4,000 Levantine Arabic tweets with detailed sentiment and topic annotations.
result Improved performance of sentiment classifiers with detailed annotations.