Adaptation of multi-domain corpus learned seeds and polarity lexicon for sentiment analysis

Sanagar, Swati; Gupta, Deepa

doi:10.1109/coconet.2015.7411166

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…Thus, it is not directly comparable with the oneto-one domain-level knowledge transfer or supervised adaptation models. A preliminary study was presented in [65]. The initial study is further extended in the current research work for genre-level multi-to-multi domain knowledge transfer using unsupervised learning.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Genre-Based Multidomain Sentiment Lexicon Learning Using Corpus-Generated Polarity Seed Words

Sanagar

Gupta

2020

IEEE Access

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Sentiment lexicon learning is of paramount importance in sentiment analysis. One of the most considerable challenges in learning sentiment lexicons is their domain-specific behavior. Transferring knowledge acquired from a sentiment lexicon from one domain to another is an open research problem. In this study, we attempt to address this challenge by presenting a transfer learning approach that creates new learning insights for multiple domains of the same genre. We propose an unsupervised sentiment lexicon learning methodology scalable to new domains of the same genre. Incremental learning and the methodology learn polarity seed words from corpora of multiple automatically selected source domains. This process then transfers its genre-level knowledge of corpus-learned seed words to the target domains. The corpus-learned seed words are used for sentiment lexicon generation for multiple target domains of the same genre. The sentiment lexicon learning process is based on the latent semantic analysis technique and uses unlabeled training data from the source and target domains. The experiment was performed using 24 domains of the same genre, i.e., consumer product review. The proposed model displays the best results using standard evaluation measures compared with the competitive baselines. The proposed genre-based unsupervised approach achieves a maximum accuracy of 86% and outperforms methods recently presented in the literature.

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Section: Discussionmentioning

confidence: 99%