Sentiment-Analysis

Can machines grasp the shades of human sentiment? Traditional opinion-mining systems often fail when language becomes ambiguous — when a review says, “The battery life is okay but could be better,” is that positive or negative? The paper “Opinion Mining Based Entity Ranking using Fuzzy Logic Algorithmic Approach” (Kalamkar & Phakatkar, 2014) offers a compelling answer: use fuzzy logic to interpret the degree of sentiment, not just its direction. At its heart, this study bridges two previously separate efforts: fuzzy-based sentiment granularity (Samaneh Nadali, 2010) and opinion-based entity ranking (Ganesan & Zhai, 2012). The innovation lies in combining fuzzy logic reasoning with conditional random fields (CRFs) to classify reviews at multiple levels of sentiment intensity, then ranking entities accordingly. In essence, it transforms vague human opinions into structured data without flattening their complexity. ...

In financial sentiment analysis, the devil has always been in the labeling. Most datasets — even the industry-standard Financial-Phrasebank — ask human annotators to tag headlines as positive, negative, or neutral. But here’s the problem: the market often disagrees. Take a headline reporting widening losses. Annotators marked it “negative.” Yet the stock rose the next day. Welcome to the disconnect. Enter FinMarBa, a bold new dataset that cuts out the middleman — the human — and lets the market itself do the labeling. Developed by Lefort et al. (2025), this 61,252-item dataset uses next-day price reactions to classify financial news, creating a labeling method that is empirically grounded, scalable, and (critically) aligned with investor behavior. ...

In the wake of the GameStop and AMC frenzies, financial firms and researchers have been racing to decode one question: Can social media sentiment predict stock prices? A new paper from researchers at Wrocław University of Science and Technology provides a sobering answer: not really. Despite employing advanced sentiment models—including a ChatGPT-annotated and emoji-savvy version of Financial-RoBERTa—the study found only weak and inconsistent relationships between sentiment and price movement for GME and AMC. ...

The idea of merging language models and financial algorithms isn’t new — but HARLF takes it a step further by embedding them in a hierarchical reinforcement learning (HRL) framework that actually delivers. With a stunning 26% annualized ROI and a Sharpe ratio of 1.2, this isn’t just another LLM-meets-finance paper. It’s a blueprint for how sentiment and structure can be synergistically harnessed. From FinBERT to Fortune: Integrating Text with Tickers Most financial LLM pipelines stop at score generation: classify sentiment and call it a signal. But HARLF builds a full sentiment pipeline using FinBERT, generating monthly sentiment scores from scraped Google News articles for each of 14 assets. These scores aren’t just inputs — they form a complete observation vector that includes: ...

Financial markets don’t reward the loudest opinions. They reward the most timely, well-calibrated ones. FinDPO, a new framework by researchers from Imperial College London, takes this lesson seriously. It proposes a bold shift in how we train language models to read market sentiment. Rather than relying on traditional supervised fine-tuning (SFT), FinDPO uses Direct Preference Optimization (DPO) to align a large language model with how a human trader might weigh sentiment signals in context. And the results are not just academic — they translate into real money. ...

When it comes to trading the S&P 500, tradition says: trust the chart. But a new study from UCLA researchers proposes a smarter compass—one that listens not only to price momentum but also to the tone of the news. By merging language model-powered sentiment scores with technical indicators and time-series forecasting, the authors build a hybrid strategy that outperforms a buy-and-hold baseline during a volatile 3-month window. ...