Sentiment Analysis of Amazon Reviews
Analyze sentiment in Amazon product reviews using VADER and TextBlob, visualize score distributions, and identify most positive and negative reviews.
Open analysis →AI Data Analysis Benchmarks for NLP
We defined practical analysis workflows from multiple domains, then ran them with AI Data Analyst using different LLM engines. On this page you can browse each workflow, open full notebook conversations, and compare model quality in shared score tables. The overall results show that modern LLMs perform very well on structured data analysis tasks.
NLP Workflow Examples
Browse reproducible AI data analysis workflows in NLP. Open any example to review prompts, conversation steps, generated code, outputs, and model-level quality scores.
Text Data EDA in Python
Explore a text dataset with word frequency analysis, top bigrams, text length distribution, and TF-IDF keyword extraction.
Open analysis →Topic Modeling with LDA in Python
Apply LDA topic modeling to a news article dataset, extract coherent topics, and visualize topic-word distributions.
Open analysis →Model Comparison for NLP
Compare LLM performance across workflows in this category. Open any score chip to jump directly to that model run and inspect the full conversation and notebook output.
Average score (0-10)
gpt-oss:120b
10.00
n=3
gemma4:31b
9.67
n=3
gpt-5.4
9.33
n=3
qwen3-coder-next
9.00
n=3
glm-5.1
8.33
n=3
qwen3.5:397b
8.00
n=3
gpt-oss:120b
Average score: 10.00/10
Scored workflows: 3
gemma4:31b
Average score: 9.67/10
Scored workflows: 3
gpt-5.4
Average score: 9.33/10
Scored workflows: 3
qwen3-coder-next
Average score: 9.00/10
Scored workflows: 3
glm-5.1
Average score: 8.33/10
Scored workflows: 3
qwen3.5:397b
Average score: 8.00/10
Scored workflows: 3
Detailed Workflow Comparison Table for NLP
This table compares model scores for each workflow in NLP. Open any score chip to jump directly to the selected model conversation and review full prompts, code, outputs, and score cards.
| Workflow | gemma4:31b | glm-5.1 | gpt-5.4 | gpt-oss:120b | qwen3-coder-next | qwen3.5:397b |
|---|---|---|---|---|---|---|
| Sentiment Analysis of Amazon Reviews sentiment-analysis-python | 10.0/10 | 8.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 4.0/10 |
| Text Data EDA in Python text-eda-python | 9.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 9.0/10 | 10.0/10 |
| Topic Modeling with LDA in Python topic-modeling-python | 10.0/10 | 7.0/10 | 8.0/10 | 10.0/10 | 8.0/10 | 10.0/10 |
What This Benchmark Shows
We tested the same step-by-step data analysis workflows across multiple LLM models and compared results using a shared scoring rubric. In NLP, most models produce strong notebook outputs with high task completion and useful analytical reasoning. Use these examples as a reference for prompt design, model selection, and workflow quality before running similar analyses on your own data in MLJAR Studio.
Start using AI for NLP
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