Proposes a semi-supervised approach to predict user-level sentiments in social media.
problem Detect and analyze sentiment in social media, especially user-level sentiments.
method Semi-supervised approach using a heterogeneous graph built from social networks, incorporating user influences and multiple types of links.
result Predicts user-level sentiments for specific topics more effectively than previous supervised learning approaches.
This paper analyzes user-level local differential privacy in distributed systems.
problem The relationship between user-level and item-level local differential privacy under the local model is complex.
method The paper analyzes the mean estimation problem and applies it to stochastic optimization, classification, and regression. It proposes adaptive strategies to achieve optimal performance at all privacy levels.
result The proposed methods are minimax optimal up to logarithmic factors and show that user-level DP can lead to faster convergence rates than item-level DP.
Paper studies user-level differential privacy in federated linear contextual bandits.
problem Federated learning with user-level differential privacy constraints.
method Unified federated bandits framework, CDP and LDP definitions, ROBIN algorithm.
result Near-optimal learning under user-level CDP with privacy budget and number of clients.
New algorithms protect user-level privacy in learning tasks.
problem Protecting user-level privacy in learning tasks with stringent constraints.
method Proposes algorithms for learning tasks under user-level differential privacy constraints.
result Privacy cost decreases as users provide more samples or as the number of users increases.
Estimates population mean from user-level data with privacy, accounting for heterogeneity.
problem Heterogeneous user data with varying numbers of data points and distributions.
method Simple model of heterogeneous user data, differential privacy mechanism for estimation.
result Asymptotic optimality of the proposed estimator and general lower bounds on error.
Novel mean estimation method under user-level differential privacy reduces noise in continual mean estimates.
problem Maintaining accurate running mean estimates under user-level differential privacy.
method Developed a novel mean estimation specific factorization under approximate differential privacy.
result Achieved asymptotically lower mean-squared error bounds in continual mean estimation.
Develops privacy-preserving methods for longitudinal linear regression.
problem Protecting individual information in longitudinal data with privacy-preserving statistics.
method Proposes a user-level private regression estimator and a privatized covariance estimator for longitudinal linear regression under user-level differential privacy.
result Establishes theoretical guarantees for practical user-level differential privacy estimation and inference in longitudinal linear regression.
New attack recovers user-level information from large batch images.
problem Recovering private information from user-level gradients in distributed learning.
method Proposes a gradient inversion attack using a denoising diffusion model as a prior.
result Demonstrates recovery of realistic facial images and private attributes.
A new algorithm estimates mean under varying user data sizes with local differential privacy.
problem Mean estimation with user-level local differential privacy under varying data sizes.
method Distribution-aware mean estimation algorithm for users with varying data sizes.
result Upper and lower bounds on the worst-case risk for mean estimation are derived.
MetaSelector learns to choose the best model for each user.
problem Heterogeneous datasets and user-specific historical data make it hard to find the best model for each user.
method Meta-learning framework to train a model selector that chooses the best model for each user based on their historical data.
result MetaSelector outperforms single model and sample-level model selector in AUC and LogLoss.
New privacy mechanism for user-level discrete distributions with reduced penalty.
problem Achieving privacy for all items of a single user in practical applications.
method Study of learning discrete distributions with user-level differential privacy, proposing a new mechanism with reduced privacy penalty.
result Proposed mechanism reduces privacy penalty to i l d e O ( k / ( m α 2 ) + k / m ε α ) ilde{\mathcal{O}}(k/(mα^2) + k/\sqrt{m}εα) i l d e O ( k / ( m α 2 ) + k / m ε α ) , significantly smaller than standard mechanisms. 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.
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.
New method uses Winsorized mean estimators for privacy-preserving statistics on dependent data.
problem Privacy-preserving statistics on dependent data with sensitive information.
method Adapting noisy Winsorized mean estimators to handle dependence via log-Sobolev inequalities.
result Asymptotic and finite sample guarantees for item-level and user-level mean estimation similar to \iid{} settings.
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…
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 …
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.
Model clusters authors and topics in short texts like social media posts.
problem Analysis of short texts is difficult due to brevity and lack of context.
method Expands Latent Dirichlet Allocation to model word dependencies and cluster users.
result Improves topic and user clustering, outperforming traditional methods.
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.
\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of unlabelled documents) is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a populati…
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.
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.
Private ALS method improves matrix completion with tighter rates and better privacy.
problem Differential privacy in matrix completion for user-level privacy.
method Joint differentially private ALS method with tighter sample complexity and privacy trade-offs.
result Achieves nearly optimal sample complexity and best privacy/utility trade-off.
We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of sentiment, price, and volatili…
MANA-Net improves market predictions by dynamically weighting news sentiments.
problem Aggregated Sentiment Homogenization in financial news data.
method Dynamic market-news attention mechanism to aggregate sentiments.
result MANA-Net outperforms recent market prediction methods by 1.1% Profit & Loss and 0.252 daily Sharpe ratio.
Python models predict stock sentiment for market-beating returns.
problem Predicting public sentiment for stock trading.
method Crowd-sourced labeled data, trained and evaluated various models.
result Best models predict market-beating returns from public sentiment.
Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment ana…
CDA framework infers channel influence from aggregated data without user identifiers.
problem Lack of user-level path data due to privacy regulations and platform restrictions.
method CDA integrates PCMCI for causal discovery and Structural Causal Model for effect estimation.
result CDA achieves strong accuracy in estimating channel influence, even under structural uncertainty.
Improved locally private sparse estimation with multiple samples per user.
problem Challenges in high-dimensional locally private sparse estimation.
method Proposes a framework for user-level locally private sparse linear regression with multiple samples per user.
result Eliminates the dependency of dimensionality on error bounds, achieving tighter error bounds.
Transformer models improve financial sentiment measurement.
problem Capturing nuanced sentiment from financial news articles.
method Transformer-based language models for sentiment classification and aggregation.
result Transformer models outperform traditional dictionary-based methods in sentiment classification.
R package sentometrics analyzes text sentiment for predictions.
problem Unlocking value from textual data using sentiment analysis.
method Optimized textual sentiment indexation with R package sentometrics.
result Forecasted CBOE Volatility Index using text sentiment data.
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
problem Evaluating if large language models exhibit company-specific biases in financial sentiment analysis.
method Comparing sentiment scores with and without company names, constructing economic models, and empirical analysis.
result LLMs show company-specific biases in sentiment analysis, impacting investor behavior and stock prices.
New algorithm optimizes privacy and utility in multi-task learning with skewed data.
problem Privacy constraints in multi-task learning with uneven data distribution.
method Adaptive reweighting of privacy budget allocation among tasks.
result Significant improvement in utility with state-of-the-art performance on benchmarks.
Study shows investor sentiment boosts intraday trading in Chinese markets.
problem Impact of investor sentiment on intraday overtrading in Chinese A-share markets.
method High-frequency sentiment indices from social media analyzed for intraday overtrading in CSI 300 and CSI 500 constituents.
result Investor sentiment significantly increases intraday overtrading, especially among institutional investors.
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.
The paper analyzes how news sentiment of companies can affect market movements.
problem Understanding how news sentiment impacts market performance and volatility.
method Applied NLP techniques to analyze news sentiment of 87 companies over 7 years.
result Strong media sentiment towards one company can indicate significant changes in sentiment towards related companies.
Study combines sentiment analysis with traditional models for better S&P 500 trading.
problem Improving trading performance in volatile markets.
method Sentiment analysis from financial news, GPT-2, FinBERT, combined with technical indicators and time-series models.
result Combining sentiment-driven insights with traditional models improves trading performance.
Research integrates sentiment analysis with reinforcement learning for better trading strategies.
problem Improving trading performance by integrating sentiment data.
method Developed a sentiment-driven trading system using a large language model and reinforcement learning.
result Sentiment signals from FinGPT improve trading performance when combined with technical indicators.
Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstandardized and widely used in the Web, especially in social media. While many datasets have been released to train sentiment …
New formula classifies product reviews into higher and lower ratings based on sentiment analysis.
problem Lack of research on using sentiment analysis for classifying text into ratings.
method Redefined sentiment proportions as a triangle structure to derive variables for classifying text into higher and lower ratings.
result Proved a dependence exists between sentiments and ratings.
Improved sentiment analysis with multimodal data.
problem Cross-modal sentiment analysis in social media, customer service, and video blogs.
method Gated mechanism for attention-based learning of cross-modal interactions, with experiments on CMU-MOSI and CMU-MOSEI datasets.
result 1.6% and 1.34% absolute improvement over state-of-the-art.
In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to …