Cross-domain sentiment classification via topical correspondence transfer

Zhou, Guangyou; Zhou, Yin; Guo, Xiyue; Tu, Xinhui; He, Tingting

doi:10.1016/j.neucom.2014.12.006

Cited by 28 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TCT [63] (topical correspondence transfer): To reduce the distribution difference by establishing the correspondence between other topics in different topics, and finally discover the topic and classify the emotions in the process of optimizing the objective function. The core element of TCT is the optimization problem, which is expressed as a joint nonnegative matrix factorization.…”

Section: Ss-fe [62] (Feature Ensemble Plus Example Selection)mentioning

confidence: 99%

A Survey of Sentiment Analysis Based on Transfer Learning

Liu

Shi

Ji³

et al. 2019

IEEE Access

146

View full text Add to dashboard Cite

With the rapid development of the Internet industry, sentiment analysis has grown into one of the popular areas of natural language processing (NLP). Through it, the implicit emotion in the text can be effectively mined, which can help enterprises or organizations to make an effective decision, and the explosive growth of data undoubtedly brings more opportunities and challenges to the sentiment analysis. At the same time, transfer learning has emerged as a new machine learning technique that uses the existing knowledge to solve different domain problems and produces state-of-the-art prediction results. Many scholars apply transfer learning to the field of the sentiment analysis. This survey summarizes the relevant research results of the sentiment analysis in recent years and focuses on the algorithms and applications of transfer learning in the sentiment analysis, and we look forward to the development trend of the sentiment analysis.INDEX TERMS Sentiment analysis, transfer learning, natural language processing.

show abstract

Section: Ss-fe [62] (Feature Ensemble Plus Example Selection)mentioning

confidence: 99%

A Survey of Sentiment Analysis Based on Transfer Learning

Liu

Shi

Ji³

et al. 2019

IEEE Access

146

View full text Add to dashboard Cite

show abstract

“…In [24], the domain specific information is learnt from several domains and unified topics are created with the help of knowledge about shared topics. Documents are represented as term document matrix.…”

Section: Topical Correspondence Transfer (Tct)mentioning

confidence: 99%

Cross Domain Sentiment Classification Techniques: A Review

Kadli¹,

Vidyavathi²

2019

IJCA

View full text Add to dashboard Cite

With the explosive growth in the availability of online resources, sentiment analysis has become an interesting topic for researchers working in the field of natural language processing and text mining. The social media corpus can span many different domains. It is difficult to get annotated data of all domains that can be used to train a learning model. Hence continuous efforts are made to tackle the issue and many techniques have been designed to improve cross domain sentiment analysis. In this paper we present literature review of methods and techniques employed for cross domain sentiment analysis. The aim of the review is to present an overview of techniques and approaches, datasets used to solve cross domain sentiment classification problem in the research work carried out in the recent years.

show abstract

“…The aim of sentiment polarity recognition is to automatically predict the sentiment polarity (e.g., positive and negative) of a piece of text. Many machine learning algorithms have been proposed for opinion-oriented information retrieval (also known as opinion mining and sentiment analysis), including unsupervised learning (Turney, 2002), supervised learning (Pang et al, 2002), graph-based semisupervised learning (Goldberg and Zhu, 2006), and matrix-based decomposition (Li et al, 2009;Zhou et al, 2015). However, these studies focus mainly on the single-domain problem, and sentiment polarity recognition of text is widely known as a domaindependent task.…”

Section: Related Workmentioning

confidence: 99%

Words alignment based on association rules for cross-domain sentiment classification

Jia

Jin

et al. 2018

Frontiers Inf Technol Electronic Eng

View full text Add to dashboard Cite

Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people's attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon R datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.

show abstract

Cross-domain sentiment classification via topical correspondence transfer

Cited by 28 publications

References 8 publications

A Survey of Sentiment Analysis Based on Transfer Learning

A Survey of Sentiment Analysis Based on Transfer Learning

Cross Domain Sentiment Classification Techniques: A Review

Words alignment based on association rules for cross-domain sentiment classification

Contact Info

Product

Resources

About