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

In the field of Natural Language Processing, information extraction fromtexts has been the objective of many researchers for years. Many differenttechniques have been applied in order to reveal the opinion that a tweet mighthave, thus understanding the sentiment of the small writing up to 280characters. Other than figuring out the sentiment of a tweet, a study can alsofocus on finding the correlation of the tweets with a certain area of interest,which constitutes the purpose of this study. In order to reveal if an area ofinterest has a trend in ongoing tweets, we have proposed an easily applicableautomated methodology in which the Daily Mean Similarity Scores that show thesimilarity between the daily tweet corpus and the target words representing ourarea of interest is calculated by using a na\"ive correlation-based techniquewithout training any Machine Learning Model. The Daily Mean Similarity Scoreshave mainly based on cosine similarity and word/sentence embeddings computed byMultilanguage Universal Sentence Encoder and showed main opinion stream of thetweets with respect to a certain area of interest, which proves that an ongoingtrend of a specific subject on Twitter can easily be captured in almost realtime by using the proposed methodology in this study. We have also compared theeffectiveness of using word versus sentence embeddings while applying ourmethodology and realized that both give almost the same results, whereas usingword embeddings requires less computational time than sentence embeddings, thusbeing more effective. This paper will start with an introduction followed bythe background information about the basics, then continue with the explanationof the proposed methodology and later on finish by interpreting the results andconcluding the findings.

A Case Study to Reveal if an Area of Interest has a Trend in Ongoing Tweets Using Word and Sentence Embeddings