Collaborative Multi-domain Sentiment Classification

Wu, Fangzhao; Huang, Yongfeng

doi:10.1109/icdm.2015.68

Cited by 51 publications

(47 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Amazon dataset contains 2000 samples for each of the four domains: book, DVD, electronics, and kitchen, with binary labels (positive, negative). Following Wu and Huang (2015), we conduct 5-way cross validation. Three out of the five folds are treated as the training set, one serves as the validation set, while the remaining is the test set.…”

Section: Multi-domain Text Classificationmentioning

confidence: 99%

“…One state-of-the-art system for MDTC, the CMSC system of Wu and Huang (2015), combines a classifier that is shared across all domains (for learning domain-invariant knowledge) with a set of classifiers, one per domain, each of which captures domain-specific text classification knowledge. This paradigm is sometimes known as the Shared-Private model (Bousmalis et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multinomial Adversarial Networks for Multi-Domain Text Classification

Chen¹,

Cardie²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

103

125

View full text Add to dashboard Cite

Many text classification tasks are known to be highly domain-dependent. Unfortunately, the availability of training data can vary drastically across domains. Worse still, for some domains there may not be any annotated data at all. In this work, we propose a multinomial adversarial network 1 (MAN) to tackle this real-world problem of multi-domain text classification (MDTC) in which labeled data may exist for multiple domains, but in insufficient amounts to train effective classifiers for one or more of the domains. We provide theoretical justifications for the MAN framework, proving that different instances of MANs are essentially minimizers of various f-divergence metrics (Ali and Silvey, 1966) among multiple probability distributions. MANs are thus a theoretically sound generalization of traditional adversarial networks that discriminate over two distributions. More specifically, for the MDTC task, MAN learns features that are invariant across multiple domains by resorting to its ability to reduce the divergence among the feature distributions of each domain. We present experimental results showing that MANs significantly outperform the prior art on the MDTC task. We also show that MANs achieve state-of-the-art performance for domains with no labeled data.

show abstract

Section: Multi-domain Text Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multinomial Adversarial Networks for Multi-Domain Text Classification

Chen¹,

Cardie²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

103

125

View full text Add to dashboard Cite

show abstract

“…More recently, Lu et al (2016) present a general regularization framework for domain adaptation while Camacho-Collados and Navigli (2017) integrate domain information within lexical resources. A popular approach within text classification learns features that are invariant across multiple domains whilst explicitly modeling the individual characteristics of each domain (Chen and Cardie, 2018;Wu and Huang, 2015;Bousmalis et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Domain Detection

Lapata

2019

Transactions of the Association for Computational Linguistics

View full text Add to dashboard Cite

In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments which are domain-heavy, i.e., sentences or phrases which are representative of and provide evidence for a given domain could enhance the robustness and portability of various text classification applications. We propose an encoderdetector framework for domain detection and bootstrap classifiers with multiple instance learning (MIL). The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.

show abstract

“…In different domains different words are used to express sentiments, and the same word may convey different sentiments in different domains [4]. To address the problem of multi-domain sentiment classification [1] has used two types of classifiers, a general sentiment classifier and a domain specific sentiment classifier. Bollegala et al has modeled sentiment classification as the problem of training a binary classifier using reviews annotated for positive or negative sentiment and also create a sentiment sensitive distributional thesaurus using labeled data for the source domain and unlabeled data for both source and target domains [3].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews

Singh¹,

Nafis²,

Mani³

2016

IJCA

View full text Add to dashboard Cite

Sentiment analysis and classification is a prominent research topic in academics as well as in industrial field. Since each customer reviews text always state emotion about a target domain, sentiment classification is a highly domain dependent task and present study considered the reviews from heterogeneous domains. Generally researchers classify the customer review with positive, negative and neutral sentiments but a positive review can be highly positive and a negative review can be highly negative, so sentiment analysis about a review can be more effective if a sentiment scale is also defined for such greater degree of positivity or negativity. We defined a framework to classify heterogeneous product reviews with degree of polarity on a sentiment scale of range -2 to 2. For each review, an intermediate form is calculated using sentiment vectors which is further processed to calculate the sentiment polarity magnitude and similarity of reviews.

show abstract

Collaborative Multi-domain Sentiment Classification

Cited by 51 publications

References 23 publications

Multinomial Adversarial Networks for Multi-Domain Text Classification

Multinomial Adversarial Networks for Multi-Domain Text Classification

Weakly Supervised Domain Detection

Sentiment Analysis and Similarity Evaluation for Heterogeneous-Domain Product Reviews

Contact Info

Product

Resources

About