Word representation has always been an important research area in the history of natural
language processing (NLP). Understanding such complex text data is imperative, given that …

The pervasive use of electronic health databases has increased the accessibility of free-text
clinical reports for supplementary use. Several text classification approaches, such as …

Sentiment analysis is a fast growing area of research in natural language processing (NLP)
and text classifications. This technique has become an essential part of a wide range of …

Topic extraction is an essential task in bibliometric data analysis, data mining and
knowledge discovery, which seeks to identify significant topics from text collections. The …

The goal of this paper is to provide an overview of the methods that allow text
representations with a focus on embeddings for text of different lengths, specifically on works …

The current decade has witnessed the remarkable developments in the field of artificial
intelligence, and the revolution of deep learning has transformed the whole artificial …

Sentiment analysis is one of the well-known tasks and fast growing research areas in natural
language processing (NLP) and text classifications. This technique has become an essential …

Subjectivity detection in the text is essential for sentiment analysis, which requires many
techniques to perceive unanticipated means of communication. Few accomplishments …

Word Embeddings are low-dimensional distributed representations that encompass a set of
language modeling and feature learning techniques from Natural Language Processing …

Sentiment analysis is a well-known and rapidly expanding study topic in natural language
processing (NLP) and text classification. This approach has evolved into a critical …