REZA NOURALIZADEH GANJI

 Google Scholar | ï LinkedIn |  ORCiD | [ ResearchGate

# rezang52@gmail.com | Ñ rezang.github.io

EDUCATION

Master of Artificial Intelligence 2020 – 2023

K. N. T. University of Technology Tehran, Iran

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Notable Courses: Natural Language Processing, Neural Networks, Recommender Systems, Information Re-

trieval, Evolutionary Computation

GPA: (18.32/20 – 3.88/4)

Bachelor of Computer (Software) Engineering 2017 – 2020

Shomal University Amol, Iran

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Notable Courses: Machine Learning, Artificial Intelligence, Algorithm Design, Data Structures, Formal Lan-

guages and Automata Theory, Engineering Probability and Statistics

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Thesis: A machine learning-based model for spam detection on mobile phone short message service (SMS);

under supervision of Dr. Hamidreza Koohi `

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Ganji, R.N. and Tohidi, N. (2025). Sentiment Analysis of Short and Incomplete Text using Transformers and

Attention Mechanism.

PAMR: Persian Abstract Meaning Representation Corpus 2 Published

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Tohidi, N., Dadkhah, C., Ganji, R.N., Sadr, E.G. and Elmi, H., 2024. PAMR: Persian Abstract Meaning Rep-

resentation Corpus. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(3),

pp.1-20.

Improving Sentiment Classification for Hotel Recommender System 2 Published

AI Researcher — Supervisor: Dr. Chitra Dadkhah K. N. T. University of Technology

Project: Advanced Sentiment Polarity Detection for Short and Incomplete Texts 2022 – 2025

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Situation: Investigated the critical challenge of sentiment analysis in short and incomplete texts, such as tweets,

where misspellings, grammatical errors, and lack of context cause traditional NLP models to fail.

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Action: Architected a novel 3-phase deep learning system for noisy text. It auto-corrects data, uses a RoBERTa

and autoencoder for denoising, and fuses features from all transformer layers for precise classification.

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Result: Achieved SOTA results for my Master’s thesis, attaining F1-scores of 89.96% on Sentiment 140 & 76.91%

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Action: Contributed to the first Persian AMR corpus, annotating 1,020 sentences by adapting guidelines for

unique Persian features. Pioneered data augmentation to generate 888 synthetic sentences from the corpus.

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Result: Co-developed and released the first Persian AMR corpus. Its use in data augmentation boosted a

sentiment analysis model’s F1-score and accuracy by 12%. The research was published in an ACM journal.

AI Researcher — Supervisor: Dr. Chitra Dadkhah K. N. T. University of Technology

Project: Enhancing Hotel RS with Deep Learning and Data Balancing 2021 – 2023

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Result: Published in CYS journal, this system achieves an 89% Macro F1-score on TripAdvisor, surpassing En-

RFBERT by 5%. Its efficient integrated architecture cuts inference time by over 60% compared to the baseline.

RESEARCH INTERESTS

LICENSES & CERTIFICATIONS

Natural Language Processing Specialization 2 Coursera

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In this four-course specialization, students learn how to construct applications for NLP activities including

The five courses in this specialization educate students how to design, develop, and optimise CNNs, RNNs,

LSTMs, and Transformers utilising Dropout, BatchNorm, Xavier/He initialization, and other approaches.

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Credential ID: K8PGAYP9BUZC

CONFERENCES & PRESENTATIONS

Neural-based approaches for sentiment analysis February 2022

KNTU University Master’s Research Seminar

Applications of Monte Carlo sampling in data mining June 2021

KNTU University Data Mining’s Research Seminar

Bio-Inspired algorithms for sentiment analysis May 2021

(RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Auto Encoders

Machine Learning: Clustering, Decision Tree, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), En-

semble Models, Logistic Regression

Math/Theory: Linear Algebra, Probability & Statistics, Multivariate Calculus, Optimization Methods

AI Packages: Pytorch, Numpy, Pandas, Matplotlib, WandB, PLotly, Scikit-learn

Languages: Persian (Farsi), English