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

Target-dependent sentiment classification remains a challenge: modeling thesemantic relatedness of a target with its context words in a sentence.Different context words have different influences on determining the sentimentpolarity of a sentence towards the target. Therefore, it is desirable tointegrate the connections between target word and context words when building alearning system. In this paper, we develop two target dependent long short-termmemory (LSTM) models, where target information is automatically taken intoaccount. We evaluate our methods on a benchmark dataset from Twitter. Empiricalresults show that modeling sentence representation with standard LSTM does notperform well. Incorporating target information into LSTM can significantlyboost the classification accuracy. The target-dependent LSTM models achievestate-of-the-art performances without using syntactic parser or externalsentiment lexicons.

Effective LSTMs for Target-Dependent Sentiment Classification