Status: Ongoing
Associated with: Thynk360
In the digital economy, online reviews can significantly influence consumer purchasing decisions. However, the rise of fake reviews which is intentionally misleading or spammy opinions undermines user trust. To combat this growing challenge, We are developing a Fake Review Detection System as part of my AI project portfolio with Thynk360. The project’s core objective is to create a robust machine learning model capable of distinguishing between genuine and fake reviews using Natural Language Processing (NLP).
Tools & Technologies Used
- Programming Language: Python
- Libraries: scikit-learn, Pandas, NumPy, NLTK
- NLP Techniques: TF-IDF vectorization, stop word removal, tokenization
- Modeling: Logistic Regression, Random Forest, and Support Vector Machine
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
Description
The project started with a labeled dataset of product reviews—half legitimate and half fabricated. After cleaning the data and conducting exploratory data analysis (EDA), I focused on converting raw text into numerical vectors using TF-IDF, which allowed the model to understand the significance of words in each review relative to the entire dataset. I also removed common stopwords, normalized text through stemming, and handled imbalanced classes using techniques such as SMOTE.
I trained multiple classification models to determine which approach offered the best performance. The Random Forest classifier emerged as the most effective, achieving over 90% accuracy. It was particularly strong at capturing word patterns and co-occurrences often found in fake reviews.
Key Highlights
- Achieved 90%+ accuracy on test data, with strong precision-recall balance.
- Used TF-IDF and NLTK to engineer features that captured semantic and syntactic nuances.
- Performed comparative model testing across Logistic Regression, SVM, and Random Forest.
- Designed the architecture to be modular and scalable, allowing easy plug-in of deep learning models in the future.
- Ensured interpretability by using feature importance analysis to identify suspicious word patterns commonly found in fake reviews.
Learned / Achieved
This project significantly strengthened my ability to work on end-to-end machine learning pipelines, from data preprocessing and feature engineering to model evaluation and deployment planning. More importantly, it enhanced my understanding of NLP in real-world applications, especially in detecting deceitful patterns that humans often overlook. I also gained insight into how imbalanced data affects classification outcomes and how crucial evaluation metrics beyond accuracy are when working with skewed datasets.
Additionally, this project introduced me to ethical considerations in AI—particularly in moderating user-generated content, maintaining transparency, and avoiding bias in automated judgments.
Future Plans
I aim to integrate deep learning models like LSTM or BERT for more nuanced language understanding. Another goal is to deploy the system as a real-time API using Flask or FastAPI, allowing integration with e-commerce platforms or review aggregators. Adding a user feedback loop to continuously improve the model’s accuracy is also on the roadmap.
