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| 辞書ベース感情分析× | 感情分析× | |
|---|---|---|
| 分野 | テキストマイニング | テキストマイニング |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年 | — | — |
| 提唱者 | — | — |
| 種類≠ | Lexicon-based NLP sentiment-scoring task | NLP text-classification task |
| 原典≠ | Nielsen, F.Å. (2011). A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs. Proceedings of the ESWC Workshop on 'Making Sense of Microposts'. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| 別名 | dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi | opinion mining, polarity detection, duygu analizi |
| 関連 | 3 | 3 |
| 概要≠ | Lexicon-based sentiment analysis computes sentiment at the word level using prebuilt sentiment dictionaries such as AFINN (Nielsen, 2011), SentiWordNet, VADER (Hutto & Gilbert, 2014), and the NRC Emotion Lexicon. It scores text by looking words up in a dictionary of charged terms, so it requires no labelled training data. | Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models. |
| ScholarGateデータセット ↗ |
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