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Record W2293959390

Identifikation von Kundenrezensionen im WWW als Basis eines Crawlers für das Opinion Mining.

2013· article· de· W2293959390 on OpenAlexvenueno aff
Sebastian Kastner, Thomas Mandl, Julia Maria Struß

Bibliographic record

VenueIngénierie des systèmes d information · 2013
Typearticle
Languagede
FieldComputer Science
TopicWeb Data Mining and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceGynecologyArtMedicine
DOInot available

Abstract

fetched live from OpenAlex

Diese Arbeit befasst sich mit der Klassifikation von Webseiten in solche, die Kundenrezensionen zu Produkten oder Dienstleistungen enthalten und solche, die keine enthalten. Die derart gewonnenen Rezensionen konnen anschliesend als Input fur Opinion-Mining-Systeme dienen, die sich mit der Extraktion und Klassifikation von Meinungen z. B. in der genannten Textsorte beschaftigen. Erste Evaluierungsergebnisse deuten mit einer Accuracy von 91 Prozent auf einen vielversprechenden Ansatz hin.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0040.018
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.269
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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