Deep learning-based sentiment analysis (SA) methods have drawn more attention in recent
years, which calls for more precise word embedding methods. This article proposes …

Most pre-trained word embeddings are achieved from context-based learning algorithms
trained over a large text corpus. This leads to learning similar vectors for words that share …

Word embeddings are used to represent words as distributed features, which can boost the
performance on sentiment analysis tasks. However, most word embeddings consider only …

Sentiment analysis (SA) is a technique that lets people in different fields such as business,
economy, research, government, and politics to know about people's opinions, which greatly …

General-purpose pre-trained word embeddings have become a mainstay of natural
language processing, and more recently, methods have been proposed to encode external …

Since some sentiment words have similar syntactic and semantic features in the corpus,
existing pre-trained word embeddings always perform poorly in sentiment analysis tasks …

Sentiment word embedding has been extensively studied and used in sentiment analysis
tasks. However, most existing models have failed to differentiate high‐frequency and low …

Word embeddings have been extensively used for sentiment analysis tasks. However,
typical existing algorithms only model the syntactic context of words but fail to capture …

Word embedding models become an increasingly important method that embeds words into
a high dimensional space. These models have been widely utilized to extract semantic and …

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 …