Sentiment & Text Analysis Project: Compare FPL Team News and Music Reviews

Hook: Tired of vague NLP projects that don’t teach you how to detect tone and bias?

Students and instructors: if you’re struggling to find a focused, real-world computational linguistics project that teaches both hands‑on sentiment analysis and critical bias detection, this guide is for you. In 2026, employers and graduate programs expect projects that show reproducible pipelines, explainability, and ethical bias audits. This project compares FPL team news (injury reports) and music reviews (example: Mitski) to teach sentiment analysis, tone detection, and bias evaluation across two contrasting genres.

Quick summary — what you’ll learn and why it matters (inverted pyramid)

Goal: Build a reproducible pipeline to mine, label, model, and audit sentiment and tone across sports team news and music reviews. You’ll practice data collection, preprocessing, labeling guidelines, lexicon and model-based sentiment, transformer fine-tuning, explainability (SHAP/LIME), and bias audits.

Why these corpora? FPL team news is typically concise, factual, hedged and sometimes conservative; music reviews are subjective, figurative, and emotionally rich. Comparing them highlights how a single sentiment model can fail across genres — a common pain point for students and practitioners.

2026 context: By late 2025 and early 2026, toolchains shifted toward instruction‑tuned transformers for few‑shot sentiment tasks, alongside stricter expectations for model explainability and bias audits in academic coursework. This project integrates those trends and emphasizes reproducibility with notebooks and Hugging Face repositories / hosting.

Project blueprint: high-level roadmap

1. Define research questions and annotation scheme.
2. Collect and store data (FPL team news + music reviews).
3. Clean and preprocess text; exploratory data analysis (EDA).
4. Baseline sentiment analysis (lexicon-based and classical ML).
5. Advanced modeling with transformer-based classifiers.
6. Tone and emotion detection, figurative language and sarcasm checks.
7. Bias audit and explainability.
8. Visualization, reporting, and reproducible deliverables.

Deliverables (student-friendly)

• A documented GitHub repo with Jupyter/Colab notebooks.
• Cleaned datasets and annotation schema (CSV/JSONL).
• Baseline and transformer models with evaluation metrics.
• Bias audit report and visualizations.
• Short presentation (5–10 slides) and a one-page README.

Step 1 — Define precise research questions

Start with focused, testable questions. Examples:

• How do sentiment distributions differ between FPL injury reports and music reviews?
• Do standard sentiment models systematically mislabel neutral, hedged FPL sentences as negative or positive?
• Can we detect author bias (favourability towards players or artists) using linguistic markers?
• How often do figurative devices in reviews (metaphor, hyperbole) cause misclassification?

Step 2 — Data collection & ethics

Sources and scraping

For FPL team news, use reputable sports outlets that publish weekly team updates (e.g., BBC Sport team news pages). For music reviews, Rolling Stone and respected music blogs provide richly descriptive text. Example inspirations: a BBC FPL team news roundup (Jan 2026) and a Rolling Stone Mitski piece (Jan 2026).

Collect at least 3k–10k sentences across both domains for a robust student project. Aim for balance (roughly equal examples per class and per domain).

Ethics & copyright

• Follow the site’s Terms of Service and robots.txt.
• For class use, prefer short excerpts or request permission for redistribution. Host only metadata and processing code; keep original articles private if needed.
• When using injury reports, be careful with sensitive medical information — treat it with privacy and ethical consideration. See guidance on designing responsible pages and disclosures in designing coming-soon & ethics.

Step 3 — Annotation scheme and labeling

A clear annotation guide is the backbone of a reliable project. Keep labels simple but expressive.

Suggested label taxonomy

• Sentiment: positive / negative / neutral
• Tone: factual / hedged / urgent / speculative / admiring
• Emotion (optional): joy, sadness, anger, fear, surprise, disgust
• Figurative: literal / metaphorical / hyperbolic / sarcastic
• Bias flag: favorable_to_subject / unfavorable_to_subject / neutral

Annotation process

• Train 3 annotators on a 200-sentence pilot set and iterate the guide.
• Measure inter-annotator agreement (Cohen’s kappa, Krippendorff’s alpha). Aim for kappa > 0.6 for sentiment.
• Resolve disagreements by majority vote or adjudication.

Step 4 — Preprocessing & EDA

Keep preprocessing minimal at first so you can analyze model errors later. Typical steps:

• Lowercase (but consider case-sensitivity for emphasis).
• Strip HTML, normalize whitespace, preserve punctuation.
• Tokenize with spaCy or Hugging Face tokenizers for transformer pipelines.
• Detect and tag URLs, mentions, and dates as placeholders.

Exploratory analyses

• Sentence length distributions across domains.
• Word frequency comparison and POS differences.
• Lexical hedges and uncertainty markers (e.g., “may”, “likely”, “could”) — common in FPL news.
• Emotion lexicon hits (NRC, VAD) — common in music reviews.

Visualization ideas: comparative bar charts of sentiment ratios, violin plots of sentence lengths, and domain-specific word clouds. These quickly reveal domain shifts that break off-the-shelf models.

Step 5 — Baselines: lexicon and classical ML

Start simple: compare lexicon-based tools (VADER, NRC, AFINN) with a logistic regression or SVM on TF-IDF features. Baselines are critical for showing the value of advanced models.

• Lexicon rules tend to do well on short, emotive reviews (music) but fail on hedged, factual sentences (FPL).
• Classical ML with n-grams captures some domain cues but struggles with figurative language.

Report metrics: accuracy, precision/recall, and macro‑F1 (important for imbalanced labels). Also include confusion matrices to see domain-specific errors.

Step 6 — Advanced models (2026 best practices)

By 2026, instruction‑tuned and domain‑adapted transformers are the standard. Recommended approaches:

• Fine-tune a transformer (e.g., RoBERTa, DeBERTa, or a distilled LLM) on your labeled dataset.
• Use few-shot instruction tuning with models if labels are scarce — many Hugging Face models support this.
• Consider multilingual models (XLM-R) if you include international press.

Practical tips:

• Use weight decay, early stopping, and learning rate schedulers to avoid overfitting small datasets.
• Perform stratified k-fold cross-validation to estimate generalization.
• For multi-label tasks (sentiment + tone), evaluate with micro and macro metrics.

Step 7 — Tone detection, figurative language & sarcasm

Sentiment is not the same as tone. Add specialized detectors:

• Tone classifier: fine-tune a model to predict hedges, certainty, or urgency. These labels are frequent in FPL injury reports (“will be assessed”, “is a doubt”).
• Figurative language detection: train or use prebuilt models to flag metaphors and hyperbole common in reviews (e.g., “a heart-wrenching masterpiece”).
• Sarcasm indicators: detect via specialized datasets or use linguistically-inspired features (punctuation, emotive intensifiers).

These modules help reduce false positives/negatives when a sentiment model is applied cross-domain.

Step 8 — Bias audit and explainability

This is the differentiator that raises a student project from “nice demo” to publishable coursework.

Explainability tools

• Use SHAP or LIME to explain individual predictions; visualize the most important tokens driving sentiment.
• For transformers, visualize attention maps and use integrated gradients for feature attribution. Platforms and APIs like recent explainability services can speed up prototyping.

Bias investigation checklist

• Does the model penalize certain player names or artist names? (Name embeddings can leak bias.)
• Does the model label hedged medical updates as negative because of words like “out” or “injury”?
• Are female artists’ reviews systematically described differently (e.g., emotional language) than male artists’?

Run controlled perturbation tests: replace subject names or swap figurative phrases and measure prediction shifts. Document any systematic skew. For guidance on detecting misinformation and manipulated media, see resources like avoiding deepfakes.

Step 9 — Evaluation & robustness checks

Beyond standard metrics, include these evaluations:

• Calibration plots to check whether confidence scores reflect true accuracy.
• Domain transfer tests: train on music reviews, test on FPL and vice versa — quantify drop in performance.
• Error analysis: sample false positives/negatives and categorize by cause (hedging, figurative language, syntax).
• Statistical tests: use bootstrap or paired t-tests to compare models reliably.

Step 10 — Visualization & reporting

Use accessible visualizations to communicate findings to non-technical stakeholders (e.g., coaches, music editors):

• Side‑by‑side sentiment distribution histograms (FPL vs reviews).
• SHAP summary plots showing token importances by domain.
• Interactive dashboards (Streamlit or Voila) to let users inspect sentences and model explanations.

Worked example: Why FPL injury reports trip up lexicon models

Consider a BBC-style FPL sentence:

"The game will come too soon for John Stones and Oscar Bobb."

A lexicon model may label this as negative because of words like “too soon” or player names occurring near injury terms. But the true pragmatics is factual, neutral reporting of availability. This mismatch demonstrates a key learning outcome: surface word cues ≠ sentiment in context.

Worked example: Music review language challenges

Sentence from a Mitski review (inspired by Rolling Stone, Jan 2026):

"A phantasmagoric quote sets the tone of Mitski’s next record..."

Here, words like “phantasmagoric” and rich metaphors carry positive appraisal through figurative language. Models must capture that appraisal beyond token polarity. Teaching students to incorporate contextual embeddings and figurative detection helps bridge this gap.

2026 trends to include in your project (practical integration)

• Instruction-tuned LLMs for few-shot classification: Use them to bootstrap labels or for model comparison; see work on instruction-tuned approaches.
• Model cards and Datasheets: Publish a Model Card (2026 best practice) documenting training data, intended use, and limitations — supported by modern explainability tooling like live explainability APIs.
• Explainability-first workflows: Evaluate models with SHAP explanations as part of model selection.
• Data-centric ML: Emphasize annotation quality and augmentation (back-translation, paraphrasing) rather than just bigger models. For broader data infrastructure thinking, see future data fabric discussions.

Evaluation rubric for instructors

Create a transparent grading rubric that maps to skills employers want:

• Data collection and ethics (15%)
• Annotation quality and inter-annotator agreement (15%)
• Baseline vs advanced modeling and performance (20%)
• Bias audit and explainability (20%)
• Reproducibility, code quality, README and presentation (20%)
• Creativity and interpretation (10%)

Common pitfalls and how to avoid them

• Collecting biased corpora — ensure balance across teams, artists, and publication types.
• Over-cleaning — removing punctuation or stop words that carry sentiment (e.g., “not”) can break models.
• Ignoring class imbalance — use stratified sampling, class weights, or focal loss.
• Confusing sentiment with bias — operationalize bias with clear definitions and tests.

Tools & starter resources (2026-ready)

• Data collection: Requests + BeautifulSoup, newspaper3k, or newspaper CLI for scraping responsibly.
• Preprocessing: spaCy (v4+), Hugging Face tokenizers.
• Lexicons: VADER (improved for social text), NRC Emotion Lexicon, AFINN.
• Modeling: Hugging Face Transformers (RoBERTa, DeBERTa), Hugging Face Datasets for dataset hosting.
• Explainability: SHAP, LIME, Captum (for PyTorch).
• Dashboards: Streamlit, Gradio for interactive demos — or mobile-friendly demos informed by the on-device creator stack.
• Reproducibility: GitHub, Binder, Docker, and a dataset card + model card. See a devops playbook for reproducible delivery here.

Suggested 8-week timeline for a semester project

1. Week 1–2: Define questions, collect pilot data, create annotation guide.
2. Week 3: Annotation and agreement testing.
3. Week 4: Baseline models and initial EDA.
4. Week 5–6: Fine-tune transformers, run cross-validation.
5. Week 7: Bias audits, explainability, robustness tests.
6. Week 8: Final report, presentation, and demo dashboard.

How to grade a compelling final submission

Look for clarity: reproducible code, clear dataset provenance, and a thoughtful discussion of limitations and ethical risks. A high-quality submission will show why the model works, when it fails, and how to mitigate harms — not just a high F1 score.

Extensions & publication ideas

• Cross-lingual studies: include French or Spanish sports outlets to study cultural framing of injuries.
• Temporal sentiment drift: track how sentiment around a player changes across a season.
• Media bias study: compare tabloids vs. official club reports for bias in coverage.
• Deploy a live demo that ingests fresh team news and flags bias for editors — consider the on-device creator delivery patterns in mobile stacks.

Final takeaways — what a student gains

• Practical skills in text mining, annotation, model building, and explainability.
• Domain intuition: why a tool that works on music reviews may fail on sports news.
• Experience performing a bias audit, a skill increasingly required in 2026 academic and industry roles.
• A portfolio-ready GitHub repo with clear deliverables and ethical documentation.

Call to action

Ready to run this project in your next class or independent study? Download our starter notebook, annotation template, and a small seed dataset tailored for this exact comparison. Fork the repo, try the baselines, and share your results — we’ll curate the best student projects and feature them in our 2026 showcase.

Get the starter materials, join the discussion forum, and submit your project: visit testbook.top/projects/sentiment-fpl-music (example link) or email classprojects@testbook.top for instructor packs and assessment rubrics.

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