REZA NOURALIZADEH GANJI
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# rezang52@gmail.com | Ñ rezang.github.io
PUBLICATIONS
Sentiment Analysis of Short and Incomplete Text 2 Published
Ganji, R.N., Tohidi, N. 2025
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Ganji, R.N. and Tohidi, N., 2025. Sentiment Analysis of Short and Incomplete Texts Using Transformer-Based
Attention Models. Journal of Electronic & Information Systems, 7(2), 132–153.
PAMR: Persian Abstract Meaning Representation Corpus 2 Published
Tohidi, N., Dadkhah, C., Ganji, R.N., Sadr, E.G., Elmi, H. 2024
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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
Ganji, R.N., Dadkhah, C., Tohidi, N. 2023
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Ganji, R.N., Dadkhah, C. and Tohidi, N., 2023. Improving Sentiment Classification for Hotel Recommender
System through Deep Learning and Data Balancing. Computaci
´
on y Sistemas, 27(3), pp.811-825.
RESEARCH EXPERIENCE
AI Researcher — Robust NLP & Representation Learning 2022 – 2025
Study: Advanced Sentiment Polarity Detection for Short and Incomplete Texts
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Motivation: The presence of short and incomplete text, such as tweets, with misspellings, grammatical errors,
and a lack of context could cause critical challenges for sentiment analysis and traditional NLP models.
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Methodology: For precise classification, a novel 3-phase system was architected to handle noisy texts. It auto-
corrects data, uses autoencoder nets for denoising, and fuses intermediate features extracted from RoBERTa
LLM.
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Findings: The requirement for human annotation is eliminated and SotA results are achieved by attaining F1-
scores of 89.96% on Sentiment 140 & 76.91% on ACL 14. The system surpassed previous baselines with a 10%
boost in accuracy.
AI Researcher — Computational Linguistics & Semantics 2021 – 2023
Study: Development and Evaluation of the First Persian AMR Corpus
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Motivation: The lack of essential semantic resources, like the AMR corpus for low-resource languages like
Persian, can restrict advanced NLP research like semantic parsing and text generation.
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Methodology: To develop the AMR corpus, 1,020 sentences were annotated by modifying guidelines for unique
Persian features. The data augmentation was performed to generate 888 synthetic sentences from the corpus.
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Findings: 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%.
AI Researcher — Deep Learning & Recommender Systems 2021 – 2023
Study: Enhancing Hotel RS with Deep Learning and Data Balancing
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Motivation: Sentiment-driven hotel recommenders exhibit bias due to imbalanced data (an excessive number
of positive reviews) and multilingual text, which degrades classification accuracy.
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Methodology: For precise recommendation, an RS was proposed to benefit from sentiment analysis. It utilized
a T5 model for data balancing via augmentation, and a cross-lingual XLM-RoBERTa classifier with an attention
mechanism used model’s hidden states.
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Findings: It achieved an 89% F1-score on the TripAdvisor data and surpassed baselines by 5%. Its efficient
integrated architecture cuts inference time by over 60% compared to the baseline.
EDUCATION
Master of Artificial Intelligence 2020 – 2023
K. N. Toosi University of Technology Tehran, Iran
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Notable Courses: Natural Language Processing, Neural Networks, Recommender Systems, Information Re-
trieval, Evolutionary Computation
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Thesis: Sentiment Analysis of Short and Incomplete Text using Transformers and Attention Mechanism; under
supervision of Dr. Chitra Dadkhah `
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Thesis Grade: (20/20 – 4/4) & 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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Thesis Grade: (20/20 – 4/4) & GPA: (17.61/20 – 3.44/4)
RESEARCH INTERESTS
v Natural Language Processing v Deep Learning v Machine Learning
v Information Retrieval v Large Language Models v Mechanistic Interpretability
TECHNICAL SKILLS
Programming: Python
Deep Learning: Transformers, Attention mechanisms, Large Language Models (LLMs), Recurrent Neural Network
(RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders
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, Hugging Face, Numpy, Pandas, Matplotlib, WandB, PLotly, Scikit-learn
Languages: Persian (Native), English (C1)
LICENSES & CERTIFICATIONS
TOEFL iBT ETS
Overall Score: 100/120 (5.0/6) — CEFR Level C1 July 2026
Natural Language Processing Specialization 2 Coursera
Younes Bensouda Mourri, Łukasz Kaiser February 2022
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In this four-course specialization, students learn how to construct applications for NLP activities including
question answering and sentiment analysis, and how to create translation, summarization, and chatbot tools.
Deep Learning Specialization 2 Coursera
Andrew NG, Kian Katanforoosh, Younes Bensouda Mourri December 2021
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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.
CONFERENCES & PRESENTATIONS
Neural-based approaches for sentiment analysis February 2022
K.N.T. University Master’s Research Seminar
Applications of Monte Carlo sampling in data mining June 2021
K.N.T. University Data Mining’s Research Seminar
Bio-Inspired algorithms for sentiment analysis May 2021
K.N.T. University Evolutionary Computation’s Research Seminar
How do search engines use machine learning methods? May 2019
Shomal University Artificial Intelligence’s Research Seminar
