# Benchmarking ChatGPT and DeepSeek in April 2025: A Novel Dual Perspective Sentiment Analysis Using Lexicon-Based and Deep Learning Approaches

arXiv: https://arxiv.org/abs/2509.19346 · HTML: https://mlsanity.com/paper/2509.19346/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP](https://mlsanity.com/paper/2309.13173/) (2023)
- [Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations](https://mlsanity.com/paper/2306.12951/) (2023)
- [DeepSeek-R1 vs. o3-mini: How Well can Reasoning LLMs Evaluate MT and Summarization?](https://mlsanity.com/paper/2504.08120/) (2025)
- [Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures](https://mlsanity.com/paper/2505.24069/) (2025)
- [A Review of DeepSeek Models' Key Innovative Techniques](https://mlsanity.com/paper/2503.11486/) (2025)
- [A Synthetic Over-sampling method with Minority and Majority classes for imbalance problems](https://mlsanity.com/paper/2011.04170/) (2020)
- [Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews](https://mlsanity.com/paper/2503.11861/) (2025)
- [AI-Enhanced Intelligent NIDS Framework: Leveraging Metaheuristic Optimization for Robust Attack Detection and Prevention](https://mlsanity.com/paper/2509.00896/) (2025)

## Abstract (arXiv)

This study presents a novel dual-perspective approach to analyzing user reviews for ChatGPT and DeepSeek on the Google Play Store, integrating lexicon-based sentiment analysis (TextBlob) with deep learning classification models, including Convolutional Neural Networks (CNN) and Bidirectional Long Short Term Memory (Bi LSTM) Networks. Unlike prior research, which focuses on either lexicon-based strategies or predictive deep learning models in isolation, this study conducts an extensive investigation into user satisfaction with Large Language Model (LLM) based applications. A Dataset of 4,000 authentic user reviews was collected, which were carefully preprocessed and subjected to oversampling to achieve balanced classes. The balanced test set of 1,700 Reviews were used for model testing. Results from the experiments reveal that ChatGPT received significantly more positive sentiment than DeepSeek. Furthermore, deep learning based classification demonstrated superior performance over lexicon analysis, with CNN outperforming Bi-LSTM by achieving 96.41 percent accuracy and near perfect classification of negative reviews, alongside high F1-scores for neutral and positive sentiments. This research sets a new methodological standard for measuring sentiment in LLM-based applications and provides practical insights for developers and researchers seeking to improve user-centric AI system design.