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 …
Learning word representations for sentiment analysis
Word embedding has been proven to be a useful model for various natural language
processing tasks. Traditional word embedding methods merely take into account word …
processing tasks. Traditional word embedding methods merely take into account word …
[PDF][PDF] Learning sentiment-specific word embedding for twitter sentiment classification
We present a method that learns word embedding for Twitter sentiment classification in this
paper. Most existing algorithms for learning continuous word representations typically only …
paper. Most existing algorithms for learning continuous word representations typically only …
Refining word embeddings for sentiment analysis
Word embeddings that can capture semantic and syntactic information from contexts have
been extensively used for various natural language processing tasks. However, existing …
been extensively used for various natural language processing tasks. However, existing …
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 …
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 …
Domain adapted word embeddings for improved sentiment classification
PK Sarma, Y Liang, WA Sethares - arXiv preprint arXiv:1805.04576, 2018 - arxiv.org
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS)
word embeddings are trained only on data from a domain of interest. This paper proposes a …
word embeddings are trained only on data from a domain of interest. This paper proposes a …
[HTML][HTML] Contextual sentiment embeddings via bi-directional GRU language model
Compared with conventional word embeddings, sentiment embeddings can distinguish
words with similar contexts but opposite sentiment. They can be used to incorporate …
words with similar contexts but opposite sentiment. They can be used to incorporate …
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 …
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 …