Fake Product Review Detection system using Aiml

Creating a Fake Product Review Detection System using AI/ML involves building a machine learning (or deep learning) pipeline that can identify whether a given review is genuine or fake.

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Inspirat We were inspired by the increasing number of manipulated product reviews on online shopping platforms like Amazon and Flipkart. These fake reviews often mislead customers, promoting poor-quality products or harming competitors unfairly. We wanted to build an AI-powered system to detect and flag these reviews automatically — protecting both customers and sellers from misinformation and manipulation.ion

What it does

Our Fake Product Review Detection System:

Analyzes product reviews (text input) and classifies them as genuine or fake

Uses Natural Language Processing (NLP) to understand the structure, tone, and sentiment of reviews

Flags suspicious reviews based on patterns like repeated content, overly generic wording, and unnatural behavior

Provides a probability/confidence score on the likelihood that a review is fake

How we built it

Data Collection

Used open-source datasets like Amazon Product Reviews, Yelp Spam Dataset, and Kaggle fake review datasets

Preprocessed the data by removing noise, stop words, and standardizing the text

Feature Engineering

Extracted textual features (TF-IDF, n-grams)

Added behavioral features: review length, frequency of posts by same user, excessive praise, etc.

Model Development

Tried multiple ML models:

Logistic Regression

Random Forest

Support Vector Machines

Deep Learning with LSTM

Transformer model (BERT) for best language understanding

Challenges we ran into

Finding good labeled data: Real vs. fake reviews aren’t always obvious or available

Ambiguity in review text: Some fake reviews sound very realistic; some genuine ones sound robotic

Imbalanced dataset: Far more real reviews than fake ones — made training harder

Overfitting: Some models performed well on training but poorly on unseen reviews

Interpreting model output: Explaining why a review is fake wasn’t always straightforward

Accomplishments that we're proud of

Achieved ~90% accuracy with our best-performing model (BERT)

Developed a working prototype that can flag suspicious reviews in real time

Built a balanced model using both linguistic and behavioral features

Successfully explained key review traits that are likely to be fake (repetition, exaggerated sentiment, etc.)

What we learned

We learnt, How NLP can be applied to real-world problems like review analysis

Importance of clean, balanced data in ML

How to use transformer models like BERT for text classification tasks

How subtle patterns in language and user behavior can signal dishonesty

Trade-offs between model performance and interpretability

What's next for Fake Product Review Detection system using Aiml

What’s Next Real-time monitoring: Integrate the model with live review systems on e-commerce platforms

Multilingual support: Expand to detect fake reviews in other languages (e.g., Hindi, Spanish)

Explainability: Add tools to explain why a review is marked fake (e.g., via LIME or SHAP)

Reviewer reputation analysis: Study user history to identify fake-review bots or paid reviewers

Collaborate with platforms to use this system for automatic moderation and flagging

Built With

  • and-kaggle-fake-review-datasets-preprocessed-the-data-by-removing-noise
  • and-standardizing-the-text-feature-engineering-extracted-textual-features-(tf-idf
  • bert)
  • best
  • data-collection-used-open-source-datasets-like-amazon-product-reviews
  • deep
  • development
  • etc.
  • excessive-praise
  • for
  • forest
  • frequency-of-posts-by-same-user
  • language
  • learning
  • logistic
  • lstm
  • machines
  • ml
  • model
  • models:
  • multiple
  • n-grams)-added-behavioral-features:-review-length
  • random
  • regression
  • stop-words
  • support
  • transformer
  • tried
  • vector
  • with
  • yelp-spam-dataset
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