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.
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 …
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…
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.
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.
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…
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.
Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often f…
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.
In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, an…
The study improves sentiment analysis of 10-K filings, revealing aggregation effects on accuracy and correlation with market outcomes.
problem Lack of sentiment analysis for 10-K filings, particularly for risk disclosures.
method Supervised lexicon-learning approach applied to 10-K filings and Item 1A risk-factor sections, trained against return and volatility labels at different levels of aggregation.
result Sentiment analysis of Item 1A sections performs better at the individual-firm level, while full-filing text is more accurate at sector and portfolio levels.
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…
BioFinBERT analyzes sentiment of biotech press releases and financial text around inflection points.
problem Analyzing sentiment of biotech press releases and financial text around inflection points.
method Finetuning BioBERT on financial datasets to create BioFinBERT for sentiment analysis.
result BioFinBERT accurately analyzes sentiment of biotech press releases and financial text around inflection points.
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.
Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernel with hyperparameters that …
Deep learning models outperform classical methods in text classification.
problem Improving text classification accuracy using deep learning.
method Comprehensive review of deep learning models and datasets for text classification.
result Deep learning models outperform classical methods on various text classification tasks.
Study uses topic modeling and sentiment analysis to uncover hedge fund performance insights.
problem Hedge fund opacity and limited disclosure make them hard to analyze.
method Applied topic modeling and sentiment analysis to hedge fund documents using DistilBERT and Top2Vec.
result Automated topic modeling and sentiment analysis can predict hedge fund performance.
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.
This paper uses deep learning to analyze sentiment in financial forums and improve stock market prediction.
problem Improving stock market prediction accuracy through sentiment analysis.
method Crawling financial forum data, training BERT model on financial corpus, and using maximum information coefficient.
result Sentiment features from financial text can reflect stock market fluctuations and improve prediction accuracy.
Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current state of the art al- gorithms for learning word embeddings learn vector representations from large corpora of text documents in an unsu- pe…
Anonymization reduces economic signal extraction from financial texts.
problem Reducing meaningful economic signals from financial texts due to anonymization.
method Analyzed the impact of anonymization on textual understanding and economic signal extraction.
result Information loss due to anonymization is severe and pervasive, outweighing its benefits in certain financial applications.
Study shows tweets about COVID-19 can predict stock market performance.
problem Understanding the impact of COVID-19 on stock markets.
method Text sentiment analysis of Twitter data to correlate tweets about COVID-19 with stock market performance.
result Strong relationship between COVID-19 sentiment and stock market performance can be predicted.
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.
How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model.…
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 …
Study compares BERT with other sentiment analysis models.
problem Comparing sentiment analysis techniques.
method Used four models: Sent WordNet, logistic regression, LSTM, and BERT on IMDB movie reviews.
result BERT outperformed other models in sentiment classification.
Our study employs sentiment analysis to evaluate the compatibility of Amazon.com reviews with their corresponding ratings. Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative. On e-commerce websites such as Amazon.com, consumers can subm…
The study distills news sources to analyze stock reactions, finding sentiment has asymmetric and sector-specific effects.
problem Analyzing the influence of financial text sources on stock reactions.
method Mixed text sources from professional platforms, blogs, and message boards were distilled using different lexica to analyze sentiment variables.
result Sentiment has an asymmetric and sector-specific effect on stock reactions.
This study analyzes app reviews to understand students' behavior in the app market.
problem Extracting sentiment from growing app reviews manually is impractical.
method Used machine learning algorithms with TF-IDF for text representation and ensemble learning for evaluation.
result SVM achieved the highest accuracy (93.37%) on tri-gram + TF-IDF scheme.
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.
Paper develops heavy-tailed embeddings for better text classification and augmentation.
problem Improving text classification, especially for extreme values.
method Develops heavy-tailed embeddings using multivariate extreme value theory and introduces a scale-invariant classifier.
result The classifier outperforms baselines and generates meaningful augmented text.
New distress dictionary improves bankruptcy prediction from disclosure text.
problem Bankruptcy prediction from financial disclosures.
method Proposes a distress dictionary based on managers' sentences, quantifies linguistic features, and builds predictive models.
result Predictive models based on the distress dictionary outperform existing methods.
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.
This paper analyzes financial sentiment using LLMs and FinBERT, improving accuracy with few-shot examples.
problem Financial sentiment analysis for market evaluation.
method Application of large language models and FinBERT, with focus on prompt engineering and few-shot learning.
result GPT-4o achieves similar sentiment classification accuracy to FinBERT with fewer examples.
Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language mode…
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.
This paper analyzes text in financial disclosures to improve financial analysis.
problem Insufficient analysis of unstructured text in financial disclosures.
method Reviews and explores methods in computational linguistics and NLP.
result Highlights limitations of sentiment metrics and suggests future research areas.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
Paper introduces new indicators for forecasting crude oil prices using short news headlines.
problem Forecasting crude oil prices from short, noisy news headlines using LDA.
method Developed two novel indicators for topic and sentiment from short text data, and applied AdaBoost.RT.
result AdaBoost.RT with the proposed indicators outperforms benchmarks in crude oil forecasting.
\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…
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the laten…
Study improves cryptocurrency price prediction using unlabeled text data.
problem Predicting cryptocurrency returns from unlabelled text data.
method Introduced weak learning approach to finetune BERT on unlabeled text data.
result Finetuning pretrained NLP models with weak labels enhances forecast accuracy.
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.
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 …
Paper generates diverse, readable adversarial texts from scratch.
problem Text classification models are easily fooled by adversarial examples.
method Trained a conditional variational autoencoder (VAE) with adversarial loss and utilized GANs to generate consistent adversarial texts.
result Successfully generates adversarial texts with higher success rate and acceptable quality.
This study uses NLP to predict stock performance based on analyst reports.
problem Predicting stock performance using textual information from analyst reports.
method Natural language processing (NLP) and a customized BERT deep learning model for Chinese text.
result Strong positive sentiment in analyst reports increases excess return and intraday volatility, while strong negative sentiment increases volatility and trading volume but decreases excess return.
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.