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 methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with similar vector representations having an opposite sentiment polarity (e.g., good and bad), thus degrading sentiment analysis performance. Therefore, this study proposes a word vector refinement model that can be applied to any pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is based on adjusting the vector representations of words such that they can be closer to both semantically and sentimentally similar words and further away from sentimentally dissimilar words. Experimental results show that the proposed method can improve conventional word embeddings and outperform previously proposed sentiment embeddings for both binary and fine-grained classification on Stanford Sentiment Treebank (SST).
Code (0)
등록된 구현이 없습니다.
Tasks
Learning Word EmbeddingsSentiment AnalysisWord EmbeddingsSimilar Papers 제목 키워드 기반
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets
This research proposal describes two algorithms that are aimed at learning word embeddings for data sparse and sentiment rich data sets. The goal is to use word embeddings adapted for domain specific data sets in downstr…
General ClassificationLearning Word EmbeddingsSentiment AnalysisSentiment Classification+2A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings
A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted …
Sentiment AnalysisWord EmbeddingsSentiment Analysis by Joint Learning of Word Embeddings and Classifier
Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current state of the art al- gorithms for learn…
Learning Word EmbeddingsSentiment AnalysisWord EmbeddingsImproving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis
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 part of a wide range of applications…
MarketingPart-Of-Speech TaggingPOSPOS Tagging+3Lexicon Integrated CNN Models with Attention for Sentiment Analysis
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentim…
Sentiment AnalysisWord Embeddings