| Title | API Pipeline for Visualising Text Analytics Features of Twitter Texts |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Fareed, Samsad Beagum Sheik |
| Conference Name | 2021 International Conference of Women in Data Science at Taif University (WiDSTaif ) |
| Date Published | mar |
| Keywords | API Pipeline, Blogs, composability, customer satisfaction, Data visualization, feature extraction, Human Behavior, IBM Watson NLU, Metrics, Pipelines, pubcrawl, Scalability, sentiment analysis, social networking (online), text analysis, text analytics, tweet analysis, Twitter API, visualization |
| Abstract | Twitter text analysis is quite useful in analysing emotions, sentiments and feedbacks of consumers on products and services. This helps the service providers and the manufacturers to improve their products and services, address serious issues before they lead to a crisis and improve business acumen. Twitter texts also form a data source for various research studies. They are used in topic analysis, sentiment analysis, content analysis and thematic analysis. In this paper, we present a pipeline for searching, analysing and visualizing the text analytics features of twitter texts using web APIs. It allows to build a simple yet powerful twitter text analytics tool for researchers and other interested users. |
| DOI | 10.1109/WiDSTaif52235.2021.9430213 |
| Citation Key | fareed_api_2021 |
API Pipeline for Visualising Text Analytics Features of Twitter Texts