NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Abstract
Reviews depict sentiments of customers towards various aspects of a product or service. Some of these aspects can be grouped into coarser aspect categories. SemEval-2014 had a shared task (Task 4) on aspect-level sentiment analysis, with over 30 teams participated. In this pa-per, we describe our submissions, which stood first in detecting aspect categories, first in detecting sentiment towards aspect categories, third in detecting aspect terms, and first and second in detecting senti-ment towards aspect terms in the laptop and restaurant domains, respectively. 1
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.
The record
- Venue
- Topic
- Sentiment Analysis and Opinion Mining
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- —
- Keywords
- Sentiment analysisComputer scienceData scienceInformation retrievalNatural language processing
- Has abstract in OpenAlex
- yes