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Paper

Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text

by Independent / Community 00012b94fe9d71237eb9c529d1172c0c51c6ee01

Free2AITools Nexus Index

64.7

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 74

P: Popularity 49

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Text Mining is a part of Neural Language Processing (NLP), also known as text analytics. Text mining includes sentiment analysis and emotion analysis which are often used in analysis on social media, news, or other media in written form. The emotional breakdown is a level of sentiment analysis that categorises text into negative, neutral, and positive sentiments. Emotion is categorized into several classes, In this study, emotion is categorized into 5 classes namely anger, fear, happiness, lo...

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Semantic Scholar 7 Citations

Paper Information Summary
Entity Passport
Registry ID	00012b94fe9d71237eb9c529d1172c0c51c6ee01
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{00012b94fe9d71237eb9c529d1172c0c51c6ee01,
  author = {Unknown},
  title = {Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00012b94fe9d71237eb9c529d1172c0c51c6ee01}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text [Paper]. Free2AITools. https://api.semanticscholar.org/00012b94fe9d71237eb9c529d1172c0c51c6ee01

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 74

Popularity (P) 49

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text: Authority (A:74), Popularity (P:49), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Text Mining is a part of Neural Language Processing (NLP), also known as text analytics. Text mining includes sentiment analysis and emotion analysis which are often used in analysis on social media, news, or other media in written form. The emotional breakdown is a level of sentiment analysis that categorises text into negative, neutral, and positive sentiments. Emotion is categorized into several classes, In this study, emotion is categorized into 5 classes namely anger, fear, happiness, lo..."

❝ Cite Node

@article{Unknown2026Feature,
  title={Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈7CitationsSemantic Scholar

🏛️74AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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downloads: 0
stars: null
forks: null
citations: 7

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