Ferreira, Liliana ; Jakob, Niklas ; Gurevych, Iryna (2008)
A Comparative Study of Feature Extraction Algorithms in Customer Reviews.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the Log Likelihood Ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2008 |
| Autor(en): | Ferreira, Liliana ; Jakob, Niklas ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | A Comparative Study of Feature Extraction Algorithms in Customer Reviews |
| Sprache: | Englisch |
| Publikationsjahr: | August 2008 |
| Buchtitel: | Proceedings of the 2nd IEEE International Conference on Semantic Computing |
| URL / URN: | https://ieeexplore.ieee.org/document/4597185 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the Log Likelihood Ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms. |
| Freie Schlagworte: | Semantic Information Management;UKP_a_SIM;UKP_p_THESEUS |
| ID-Nummer: | TUD-CS-2008-100 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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| Suche nach Titel in: | TUfind oder in Google |
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