Information transfer between time series is calculated by using the asymmetric information-theoretic measure known as transfer entropy. Geweke's autoregressive formulation of Granger causality is used to find linear transfer entropy, and Schreiber's general, non-parametric, information-theoretic formulation is used to …
Detecting and aggregating sentiments toward people, organizations, and events expressed in unstructured social media have become critical text mining operations. Early systems detected sentiments over whole passages, whereas more recently, target-specific sentiments have been of greater interest. In this paper, we pres…
Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, so…
Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in the text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed…
SentiCite analyzes citations for sentiment and nature, improving on existing methods.
problem Identifying quality scientific work amidst many citations.
method Sentiment analysis of citations with motivation detection.
result SentiCite outperforms state-of-the-art methods with a F1-measure of 0.71.
Proposes RTL model for sentiment classification and key word detection in online reviews.
problem Sentiment classification and key word detection in online reviews for hospitality industry.
method Regularized Text Logistic (RTL) regression model.
result RTL model achieves satisfactory classification performance and identifies key word features.
NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy la…
This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the…
Paper benchmarks Bengali language classification tasks using MConv-LSTM network.
problem Lack of computational resources for NLP tasks in under-resourced languages like Bengali.
method Built three datasets, BengFastText word embeddings, and MConv-LSTM network for hate speech detection, document classification, and sentiment analysis.
result BengFastText yields up to 92.30%, 82.25%, and 90.45% F1-scores in document classification, sentiment analysis, and hate speech detection respectively.
Paper proposes deep learning for fake claim detection on social media.
problem Spread of false information on social media.
method Extended LIAR dataset with sentiment analysis; BERT-Base model for classification.
result 70% accuracy in classifying claims as genuine or fake.
New method explains high-dimensional text classifiers.
problem Limited explainability tools for high-dimensional inputs and neural networks.
method Theoretical high-dimensional properties in neural networks.
result Improved explainability for neural network classifiers.
StockEmotions dataset for financial sentiment and emotion analysis.
problem Limited resources for financial sentiment analysis.
method Collects 10,000 English comments from StockTwits, categorizes emotions into 12 classes.
result DistilBERT outperforms other models in sentiment classification, and Temporal Attention LSTM model achieves best performance in multivariate time series forecasting.
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.
Study investigates micro-event detection on FLOSS version releases from Stack Overflow.
problem Detecting micro-events in FLOSS version release events from textual messages.
method Developed pipelines using LDA topic modeling, hSBM topics, and sentiment analysis; optimized feature spaces with RFECV; evaluated models with statistical analysis.
result Found characteristic changes in topics or sentiment features before or after FLOSS version releases.
Deep learning models detect and classify log anomalies.
problem Anomaly detection in unstructured log data.
method Auto-LSTM, Auto-BLSTM, and Auto-GRU models for feature extraction.
result Models outperform other algorithms on various log data sets.
The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. In recent research, a substantial effort has been invested to develop sophisticated financial polarity-lexicons that can be used to investigate how financial sentiments relate to future company performance. However, …
Predicting market volatility from financial news and tweets.
problem Quantifying future volatility and returns in financial modeling.
method Topic modeling and sentiment analysis of financial news and tweets.
result Positive sentiment in tweets is negatively correlated with market volatility.
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others. Most of the recent work on multimodal fusion does not guarantee the fidelity of the multimodal representation with respect to the unimodal representations. In this paper, we prop…
This research secures deployed sentiment analysis models by identifying and defending against attack vectors.
problem Securing deployed machine learning models, particularly sentiment analysis systems, from adversarial attacks.
method BAD (Build, Attack, Defend) Architecture, evaluating two implementations.
result Demonstrated a viable methodology for securing machine learning models in production.
Study predicts market bubbles using machine learning and financial news sentiment.
problem Predicting market bubbles in the S&P 500 index.
method Three-step approach combining financial news sentiment and macroeconomic indicators.
result Proposed three-step ensemble approach significantly improves bubble prediction accuracy.
The paper analyzes sports commentary to automatically recognize events and extract insights.
problem Automatically recognizing and categorizing major actions in sports events from commentary.
method Used multiple Natural Language Processing techniques for classification and sentiment analysis.
result Identified insights from analyzing live sport commentaries and classifying major actions.
Graph-based semi-supervised learning for aspect term extraction.
problem Aspect term detection and sentiment analysis in reviews.
method Label spreading algorithm on graph sparsified by kNN for efficient classification.
result The approach achieves good results with limited labeled data.
Researchers develop method to protect against 'weight poisoning' attacks on pre-trained models.
problem The security threat of downloading untrusted pre-trained weights that can be manipulated after fine-tuning.
method RIPPLe regularization method and Embedding Surgery initialization procedure.
result Demonstrated that weight poisoning attacks are possible even with limited knowledge of the dataset and fine-tuning procedure.
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
problem Traditional trading strategies fail during high volatility and regime shifts.
method Combines trend-following, mean-reversion, sentiment analysis, machine learning, and market regime filtering.
result Hybrid model achieved 135.49% return on investment over 24 months.
Analyzes news graphs to predict financial market dislocations.
problem Predicting financial market dislocations using news content.
method Extracts entities from news articles, aggregates them into graphs, applies network analysis, and uses sentiment analysis.
result Identifies high entropy in news graphs correlates with financial market dislocations.
The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, hum…
The paper improves cryptocurrency price forecasting using deep learning and NLP on financial, blockchain, and social media data.
problem Improving cryptocurrency price forecasting accuracy and profitability.
method Integrates financial, blockchain, and social media data; applies BART MNLI model for sentiment analysis; uses deep learning NLP models; compares with traditional methods; uses local extrema as predictive targets.
result Significantly improves forecasting accuracy and profitability of cryptocurrency price predictions.
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.
Project analyzes drug reviews to predict ratings using machine learning.
problem Predicting drug ratings from text reviews.
method Implemented supervised machine learning algorithms with TFIDF and Count Vectors.
result Good results in predicting test data sets for popular conditions.
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.
Over the last few years, machine learning over graph structures has manifested a significant enhancement in text mining applications such as event detection, opinion mining, and news recommendation. One of the primary challenges in this regard is structuring a graph that encodes and encompasses the features of textual …
Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily …
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.
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.
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.