Roadmap for Polarity Lexicon Learning and Resources: A Survey

Sanagar, Swati; Gupta, Deepa

doi:10.1007/978-3-319-47952-1_52

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Therefore, the use of only standard seed words or existing sentiment lexicons may be insufficient for creating a domain-based lexicon that can learn the domain-relevant polarity, domain-relevant polarity score, and domainspecific sentiment words. The generation of a sentiment lexicon that is scalable to multiple new domains remains an open research problem [26,27].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%