Refined global word embeddings based on sentiment concept for sentiment analysis
Y Wang, G Huang, J Li, H Li, Y Zhou, H Jiang - Ieee Access, 2021 - ieeexplore.ieee.org
Sentiment Analysis is an important research direction of natural language processing, and it
is widely used in politics, news and other fields. Word embeddings play a significant role in …
is widely used in politics, news and other fields. Word embeddings play a significant role in …
Sentiment analysis based on improved pre-trained word embeddings
SM Rezaeinia, R Rahmani, A Ghodsi, H Veisi - Expert Systems with …, 2019 - Elsevier
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 …
and text classifications. This technique has become an essential part of a wide range of …
Bayesian estimation‐based sentiment word embedding model for sentiment analysis
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 …
tasks. However, most existing models have failed to differentiate high‐frequency and low …
Refining word embeddings using intensity scores for sentiment analysis
Word embeddings that provide continuous low-dimensional vector representations of words
have been extensively used for various natural language processing tasks. However …
have been extensively used for various natural language processing tasks. However …
SentiVec: Learning sentiment-context vector via kernel optimization function for sentiment analysis
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 …
years, which calls for more precise word embedding methods. This article proposes …
Encoding sentiment information into word vectors for sentiment analysis
Z Ye, F Li, T Baldwin - … of the 27th international conference on …, 2018 - aclanthology.org
General-purpose pre-trained word embeddings have become a mainstay of natural
language processing, and more recently, methods have been proposed to encode external …
language processing, and more recently, methods have been proposed to encode external …
Improving the accuracy of pre-trained word embeddings for sentiment analysis
SM Rezaeinia, A Ghodsi, R Rahmani - arXiv preprint arXiv:1711.08609, 2017 - arxiv.org
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 …
language processing (NLP) and text classifications. This technique has become an essential …
Adversarial learning of sentiment word representations for sentiment analysis
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 …
performance on sentiment analysis tasks. However, most word embeddings consider only …
Sentiment embeddings with applications to sentiment analysis
We propose learning sentiment-specific word embeddings dubbed sentiment embeddings
in this paper. Existing word embedding learning algorithms typically only use the contexts of …
in this paper. Existing word embedding learning algorithms typically only use the contexts of …
Cross-domain sentiment aware word embeddings for review sentiment analysis
Learning low-dimensional vector representations of words from a large corpus is one of the
basic tasks in natural language processing (NLP). The existing universal word embedding …
basic tasks in natural language processing (NLP). The existing universal word embedding …