Prepare an author-labelled corpus
Collect multiple texts per author, ensure enough samples per author, and balance or record class-imbalance for evaluation.
01 / THE IDEA
Author identification in practice.
Author identification (authorship attribution) maps text samples to their likely author using stylistic, lexical and syntactic features. Important failure modes include short texts, topical confounds (topic vs author), cross-domain writing, and deliberate obfuscation.
Real-world use case · Proposed extension
Proposed application for publishers or editors: flag likely author mismatches in anonymised or multi-author drafts so a human reviewer can inspect and confirm. This extension is not provided by the supplied starter.
02 / THE BUILD
How to build a author identification system project
step by step.
Extract stylistic features
Use TF-IDF n‑grams, character n‑grams and function-word frequencies or transformer embeddings to capture author style beyond topic words.
Train and validate a classifier
Fit a multi-class classifier with cross-validation grouped by document to avoid leakage, and report macro F1 and confusion matrices across authors.
Surface results for human review
Provide top-k author candidates, example supporting phrases, and allow a reviewer to correct labels and mark uncertain cases.
03 / SOURCE SNAPSHOT
What the author identification system starter actually does.
The supplied starter defines six hand-written sentences with binary labels, splits them into train and test, fits a TF-IDF (unigram+bigram) + LogisticRegression pipeline, prints a classification report on the tiny test split and shows one sample prediction. It does not use an author-labelled corpus or multi-author labels.
SOURCE EXCERPTPython
model = Pipeline(
steps=[
("tfidf", TfidfVectorizer(ngram_range=(1, 2), min_df=1)),
("classifier", LogisticRegression(max_iter=1000)),
]
)
model.fit(X_train, y_train)Important limitation
The code uses six generic sentences with binary labels and therefore contains no author identities or multi-author data; it cannot validate author identification or assess per-author performance.
04 / BEYOND THE CLASSROOM
Where a author identification system project could go next.
PROPOSED BUSINESS CONCEPT
Author identification: a pilot concept
Proposed extension: gather a small multi-author corpus (several authors, many samples per author), extract stylistic features, and train a multi-class author classifier that shows top candidates and supporting text snippets for human review.
WHO IT COULD SERVEPublishers and editors
FIRST MVPA reproducible pipeline that trains a TF-IDF + classifier on N labelled authors, reports macro F1 and a confusion matrix, and returns top‑3 author candidates with example evidence for each prediction.
KEY RISKAuthorship attribution can be legally sensitive and prone to misattribution, especially on short or edited texts; a qualified human reviewer must remain in charge and this prototype must not be used as sole evidence in legal or HR decisions.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a author identification system project puts on your resume.
- Assemble and split an author-labelled corpus so no author leakage occurs between splits.
- Extract TF-IDF, character n‑grams and simple stylistic features for author modelling.
- Train and evaluate a multi-class author classifier and report macro F1 and per-author confusion.
- Design a reviewable output that shows top candidates and supporting text for human verification.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Author Identification System project FAQs.
- Is this suitable as a final year project?
- Yes — the topic is appropriate for a final year project; expand beyond the starter by collecting multi-author corpora, addressing short-texts and topical confounds, and adding robust evaluation.
- What skills and tools will I need?
- You should know Python, scikit-learn, and spaCy, plus NLP concepts like tokenisation, n‑grams, stylometry, feature engineering, and evaluation metrics for multi-class tasks.
- How should I evaluate progress and how long will it take?
- Start with dataset collection and baseline models, then iterate with feature and domain experiments; a focused honours project can take 3–6 months depending on dataset size and experiments.