Financial News Sentiment Classification
How do classic vectorization techniques compare against deep neural network architectures in classifying market sentiment from condensed financial headlines?
Built an NLP pipeline to evaluate sentiment polarity in financial news. Compared Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) feature engineering across Random Forest and Deep Neural Network (DNN) classifiers, achieving 79% accuracy.
Problem Framing and Analytical Objective
Financial market sentiment can shift within seconds of headline publication. Unlike general conversational text, financial journalism employs subtle linguistic nuance, domain-specific terminology, and condensed sentence structures where single words ('cut', 'beat', 'drag', 'soar') dictate market sentiment direction.
This project explored the trade-offs between traditional statistical text representations and dense neural network models in classifying financial sentiment accurately.
Analytical Steps and Modeling Workflow
Text Preprocessing & Tokenization
Cleaned headline corpora using custom financial stopword filters, lemmatization, and n-gram extraction to preserve critical phrases like "rate cut" or "revenue miss".
Vectorization Modeling
Constructed comparative feature sets utilizing Bag-of-Words (BoW) frequency matrices and TF-IDF representations with sublinear term frequency scaling.
Classifier Architectures
Trained and tuned Random Forest ensembles and multi-layer Deep Neural Networks (DNN) with dropout regularization to avoid overfitting on high-dimensional text vectors.
Evaluation & Error Analysis
Assessed precision, recall, and F1-scores across positive, neutral, and negative sentiment classes, performing detailed error analysis on ambiguous headlines.
Baseline Assumptions and Parameter Inputs
- Labeled ground truth dataset validated against financial domain annotator benchmarks.
- Independent headline evaluation without incorporating external price action context.
Analytical Judgement and Observed Insights
TF-IDF with unigrams and bigrams paired with Deep Neural Networks achieved a peak test accuracy of 79.2%, outperforming basic BoW Random Forest by 7.4%.
Neutral headlines presented the highest classification difficulty due to factual reporting lacking explicit emotional vocabulary, illustrating the necessity of financial domain embeddings.
Critical Limitations and Uncertainty Boundaries
- Bag-of-Words and TF-IDF representations discard sequential word order; modern transformer architectures (e.g. FinBERT) capture contextual relationships more comprehensively.