Novel ABC: Aspect Based Classification of Sentiments Using Text Mining for COVID-19 Comments

Intricate text mining techniques encompass various practices like classification of text, summarization and detection of topic, extraction of concept, search and retrieval of ideal content, document clustering along with many more aspects like sentiment extraction, text conversion, natural language processing etc. These practices in turn can be used to discover some non-trivial knowledge from a pool of text-based documents. Arguments, difference in opinions and confrontations in the form of words and phrases signify the knowledge regarding an ongoing situation. Extracting sentiment from text that is gathered from online networking web-based platforms entitles the task of text mining in the field of natural language processing. This paper presents a set of steps to optimize the text mining techniques in an attempt to simplify and recognize the aspect-based sentiments behind the content obtained from social media comments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Text-Based Analysis of COVID-19 Comments Using Natural Language Processing

In dialectology, Natural Language Processing is the process of recognizing the various ontologies of words generated in human language. Various techniques are used for analyzing the corpus from naturally generated content by users on various platforms. The analysis of these textual contents collected during the COVID-19 has become a goldmine for marketing experts as well as for researchers, thus making social media comments available on various platforms like Facebook, Twitter, YouTube, etc., a popular area of applied artificial intelligence. Text-Based Analysis is measured as one of the exasperating responsibilities in Natural Language Processing (NLP). The chief objective of this paper is to work on a corpus that generates relevant information from web-based statements during COVID-19. The findings of the work may give useful insights to researchers working on Text analytics, and authorities concerning to current pandemic. To achieve this, NLP is discussed which extracts relevant information and comparatively computes the morphology on publicly available data thus concluding knowledge behind the corpus. © 2022, Springer Nature Switzerland AG.