Sentiment analysis with covariate-assisted word embeddings
نویسندگان
چکیده
منابع مشابه
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 contextbased 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 an...
متن کاملSentiment analysis leveraging emotions and word embeddings
Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments e...
متن کاملTopic Sentiment Joint Model with Word Embeddings
Topic sentiment joint model is an extended model which aims to deal with the problem of detecting sentiments and topics simultaneously from online reviews. Most of existing topic sentiment joint modeling algorithms infer resulting distributions from the co-occurrence of words. But when the training corpus is short and small, the resulting distributions might be not very satisfying. In this pape...
متن کاملSentiment 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 useful for performing natural language processing tasks. Current state of the art algorithms for learning word embeddings learn vector representations from large corpora of text documents in an unsupervised fashion. This paper introduces SWESA (Supervised Word Embeddings for Sentiment...
متن کاملA Convolution Kernel for Sentiment Analysis using Word-Embeddings
Accurate analysis of a sentence’s sentiment requires understanding of how words interact to convey emotion. Current works use the sentence’s parse tree and recursively compute sentiment of the constituent phrases. This approach is expensive and requires a human to annotate all subtrees of a sentence. We examine how using a lexical similarity kernel can leverage word-embeddings, generated in an ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2021
ISSN: 1935-7524
DOI: 10.1214/21-ejs1854