Journal ArticleDOI
An enhanced sentiment dictionary for domain adaptation with multi-domain dataset in Tamil language (ESD-DA)
E. Sivasankar,K. Krishnakumari,P. Balasubramanian +2 more
- Vol. 25, Iss: 5, pp 3697-3711
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TLDR
This work intends to classify reviews of multiple target domains in Tamil by using the unified dictionary with a large number of vocabularies that significantly improves the accuracy of DA with the other baseline methods and handles many words in multiple domains with ease.Abstract:
Mostly sentiment analysis employs dictionary approaches for recognizing the polarity of terms in a review. However, in sentiment analysis between different domains called domain adaptation (DA), the sentiment lexicon disappoints that leads to the feature mismatch problem. Now, many e-commerce sites try to process reviews in their native languages. In this paper, we propose an enhanced dictionary in our native language (Tamil) that aims at building contextual relationships among the terms of multi-domain datasets that tries to minimize the feature mismatch problem. The proposed dictionary employs both labeled and unlabeled data from the source domain and unlabeled data from the target domain. More precisely, the initial dictionary explores pointwise mutual information for calculating contextual weight then the final dictionary estimates the rank score based on the importance of terms among all the reviews. This work intends to classify reviews of multiple target domains in Tamil by using the unified dictionary with a large number of vocabularies. This extendible dictionary significantly improves the accuracy of DA with the other baseline methods and handles many words in multiple domains with ease.read more
Citations
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Journal ArticleDOI
Sentiment lexicon for cross-domain adaptation with multi-domain dataset in Indian languages enhanced with BERT classification model
TL;DR: An augmented sentiment dictionary is developed in the authors' native regional language (Tamil) that intends to construct the contextual links between terms in multi-domain datasets to reduce problems like polarity mismatch, feature mismatch, and polysemy.
Proceedings ArticleDOI
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
TL;DR: In this paper , the authors used Support Vector Machine (SVM) and Deep Learning (DL) algorithms for NLP in Tamil language and achieved higher accuracy than other ML techniques.
Proceedings ArticleDOI
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil
TL;DR: In this paper , the authors used Support Vector Machine (SVM) and Deep Learning (DL) algorithms for NLP in Tamil language and achieved higher accuracy than other ML techniques.
References
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Posted Content
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
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Unsupervised Domain Adaptation by Backpropagation
Yaroslav Ganin,Victor Lempitsky +1 more
TL;DR: In this paper, a gradient reversal layer is proposed to promote the emergence of deep features that are discriminative for the main learning task on the source domain and invariant with respect to the shift between the domains.
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Unsupervised Domain Adaptation by Backpropagation
Yaroslav Ganin,Victor Lempitsky +1 more
TL;DR: The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-of-the-art on Office datasets.
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Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
TL;DR: This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.