English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the key tasks of sentiment analysis of product reviews is to extractproduct aspects or features that users have expressed opinions on. In thiswork, we focus on using supervised sequence labeling as the base approach toperforming the task. Although several extraction methods using sequencelabeling methods such as Conditional Random Fields (CRF) and Hidden MarkovModels (HMM) have been proposed, we show that this supervised approach can besignificantly improved by exploiting the idea of concept sharing acrossmultiple domains. For example, "screen" is an aspect in iPhone, but not onlyiPhone has a screen, many electronic devices have screens too. When "screen"appears in a review of a new domain (or product), it is likely to be an aspecttoo. Knowing this information enables us to do much better extraction in thenew domain. This paper proposes a novel extraction method exploiting this ideain the context of supervised sequence labeling. Experimental results show thatit produces markedly better results than without using the past information.

Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results