Identifying neutral reviews from unlabeled data: An exploratory study on user ratings and word-level polarity scores
The presence of the reviews containing mixed or contrasting opinions, also known as neutral reviews, is prevalent in user feedback data. By leveraging annotated data, supervised machine learning (ML) classifiers can learn implicit patterns to identify these neutral reviews. However, labeled data are barely available in most circumstances. When annotated data are unavailable, unsupervised approaches such as lexicon-based methods are employed that utilize word-level polarity scores with a set of rules. As a preliminary study for developing a sophisticated unsupervised framework for recognizing neutral reviews, here, we scrutinize the performances of the existing lexicon-based methods. When app- doi
- 10.1145/3511095.3536367
- name
- Identifying neutral reviews from unlabeled data: An exploratory study on user ratings and word-level polarity scores
- source
- supplied-acm-publisher-html-and-archival-pdf
- license
- © 2022 Copyright held by the owner/author(s).
- summary
- The presence of the reviews containing mixed or contrasting opinions, also known as neutral reviews, is prevalent in user feedback data. By leveraging annotated data, supervised machine learning (ML) classifiers can learn implicit patterns to identify these neutral reviews. However, labeled data are barely available in most circumstances. When annotated data are unavailable, unsupervised approaches such as lexicon-based methods are employed that utilize word-level polarity scores with a set of rules. As a preliminary study for developing a sophisticated unsupervised framework for recognizing neutral reviews, here, we scrutinize the performances of the existing lexicon-based methods. When app
- acm_source
- ACM Digital Library PDF: https://dl.acm.org/doi/10.1145/3511095.3536367
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- full_text
- open_access
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- displayAuthor
- Salim Sazzed
- displayPublishTime
- 2022-06-28
- acm_source_attribution
- Supplied ACM publisher HTML, verified against authorized ACM archival PDF; complete full-text conversion with page facsimiles.
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