From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification

arXiv cs.CL·Dip Biswas Shanto, Mitali Yadav, Prajwal Panth, Suresh Chandra Satapathy

15h ago

·~1 min·5/22/2026·en·0

Quick Take

This paper compares various ML models for sentiment classification, highlighting RoBERTa's superior performance.

Key Points

Examines sentiment analysis in movie reviews.
Evaluates models including Naive Bayes and transformers.
RoBERTa achieved 93.02% accuracy in classification.

Reader Mode unavailable (could not extract clean content).

Read on arxiv.org

Want this in your inbox every morning?

Daily brief at your local 8am — bilingual EN/中文, free.

Subscribe — it's free

More from arXiv cs.CL

See more →

arXiv cs.CL·Leyao Wang, Yanan He, Peng Chen, Asaf Yehudai, Yixin Liu, Rex Ying, Michal Shmueli-Scheuer, Arman Cohan

2d ago

FeaturedOriginal

Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?

AI Summary

The reliability of LLM judges for evaluating deep research agents is critically assessed using the REFLECT benchmark.

#LLM #Agent #Inference #Policy

2

arXiv cs.CL·Xiaoou Liu, Tiejin Chen, Dengjia Zhang, Yaqing Wang, Lu Cheng, Hua Wei

2d ago

FeaturedOriginal

Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

AI Summary

The Stepwise Confidence Attribution framework enhances diagnosis of reasoning failures in black-box LLMs.

#LLM #Inference #Open Source

4

arXiv cs.CL·Geoffrey Martin, Xuan Zhong Feng, Yifan Peng

15h ago

FeaturedOriginal

Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity

AI Summary

LLMs outperform fine-tuned models in extracting complex circumstances from NVDRS data.

#LLM #AI Coding #Inference

0

Related in this space

See more →

arXiv cs.AI·Angelos Angelopoulos, James F. Cahoon, Ron Alterovitz

3d ago

FeaturedOriginal

From Prompts to Protocols: An AI Agent for Laboratory Automation

AI Summary

An AI agent integrates large language models for automating laboratory protocols, enhancing efficiency and accuracy.

#LLM #Agent #AI Coding #Enterprise AI

1

arXiv cs.AI·Yihan Xia, Panpan You, Taotao Wang, Fang Liu, Han Qi, Xiaoxiao Wu, Shengli Zhang

2d ago

FeaturedOriginal

Agentic Trading: When LLM Agents Meet Financial Markets

AI Summary

The paper reviews LLM-based trading agents, highlighting protocol incomparability and reproducibility challenges.

#LLM #Agent #AI Startup #Enterprise AI

3

33

Business impact20%0

Novelty (recency)10%96

≥75 high · 50–74 medium · <50 low

Why Featured

The paper's findings signal that adopting advanced models like RoBERTa can significantly enhance sentiment analysis accuracy, crucial for developers and PMs in building effective AI-driven applications.