Results 131 to 140 of about 265,995 (181)

Unsupervised Twitter Sentiment Classification

Proceedings of the International Conference on Knowledge Management and Information Sharing, 2014
Sentiment classification is not a new topic but data sources having different characteristics require customized methods to exploit the hidden existing semantic while minimizing the noise and irrelevant information. Twitter represents a huge pool of data having specific features.
Andrei Bacu, Mihaela Dinsoreanu
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Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings

IEEE Transactions on Knowledge and Data Engineering, 2016
Unsupervised Cross-domain Sentiment Classification is the task of adapting a sentiment classifier trained on a particular domain (source domain), to a different domain (target domain), without requiring any labeled data for the target domain. By adapting an existing sentiment classifier to previously unseen target domains, we can avoid the cost for ...
Bollegala, Danushka, Mu, Tingting, Goulermas, John Yannis   +2 more
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Object semantics sentiment correlation analysis enhanced image sentiment classification

Knowledge-Based Systems, 2020
Abstract With the development of artificial intelligence and deep learning, image sentiment analysis has become a hotspot in computer vision and attracts more attention. Most of the existing methods focus on identifying the emotions by studying complex models or robust features from the whole image, which neglects the influence of object semantics on
Jing Zhang, Mei Chen, Han Sun, Dongdong Li, Zhe Wang   +4 more
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Knowledge-oriented Sentiment-level Embedding for Sentiment Classification

Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, 2019
Sentiment classification in document-level is an important task in Sentiment Analysis (SA). The existing methods learn mainly information from data for identifying the sentiment polarity of a document. We reveal that the sentiment information such as polarity can be an important external knowledge resource for classification. Our proposals are based on
Xiaoran Xu, Pengfei Li
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Sentiment classification of short text using sentimental context

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC), 2017
Sentiment analysis has important applications in many areas, including marketing, recommendation, and financial analysis. Since topic modeling can discover hidden semantic structures, researchers put forward sentiment analysis models based on topic models.
Wenjie Zheng, Zenan Xu, Yanghui Rao, Haoran Xie, Fu Lee Wang, Reggie Kwan   +5 more
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Aspect Sentiment Classification with Document-level Sentiment Preference Modeling

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
In the literature, existing studies always consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect, which largely ignore the document-level sentiment preference information, though obviously such information is crucial for alleviating the information deficiency problem in ASC.
Xiao Chen, Changlong Sun, Jingjing Wang, Shoushan Li, Luo Si, Min Zhang, Guodong Zhou   +6 more
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Sentiment classification of tweets using hierarchical classification

2016 IEEE International Conference on Communications (ICC), 2016
This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them into one of three classes: positive, negative or neutral.
Afroze Ibrahim Baqapuri, Saad Saleh, Muhammad U. Ilyas, Muhammad Murtaza Khan, Ali Mustafa Qamar   +4 more
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Sentiment Classification

2015
In this work, we focus on the application of text mining and sentiment analysis techniques for analyzing Tunisian users' statuses updates on Facebook. We aim to extract useful information, about their sentiment and behavior, especially during the “Arabic spring” era. To achieve this task, we describe a method for sentiment analysis using Support Vector
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Combine sentiment lexicon and dependency parsing for sentiment classification

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration, 2013
With the rapid development of internet technology and e-commerce sites, there are more and more products review in the network. People are willing to make a survey on the internet before purchasing the products. The automatic identification of the sentiment of comments is necessary.
Changqin Quan, Xiquan Wei, Fuji Ren
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Sentiment-Polarized Word Embedding for Multi-label Sentiment Classification

2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2018
Sentiment analysis of text is an import branch of natural language process. In this paper, we propose a sentiment-polarized word embedding model (SPWE) with emotional dictionary, which is a variant of the C&W. Our model is able to represent and differentiate the emotional semantic of words, which is critical in sentiment classification tasks. This weak
Liujie Zhang, Yanquan Zhou, Ruiqi Chen, Xiuyu Duan   +3 more
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