Sentiment Lexicon Expansion Based on Neural PU Learning, Double
            Dictionary Lookup, and Polarity Association

Wang, Yasheng; Zhang, Yang; Liu, Bing

doi:10.18653/v1/d17-1059

Cited by 43 publications

(16 citation statements)

References 28 publications

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“…The approach used NRC, Hu & Liu, and four other lexicons. A multilayer perceptron (MLP) approach used a positive and unlabeled learning approach to identify sentiment words from corpora [44]. This approach performed a double dictionary search in a Chinese dictionary and classified social media instances.…”

Section: A Dictionary-based Approachesmentioning

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: A Dictionary-based Approachesmentioning

confidence: 99%

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

Sanagar

Gupta

2020

IEEE Access

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“…This sentiment information has great commercial value and relevant influence on public opinion or merchandise sales. Many techniques of text sentiment analysis, such as sentiment lexicons [4]- [8], word representation [9], [10], deep learning [3], [11], [12] and capsule networks [13], [14] have been studied to explore the values enclosed in the comments.…”

Section: Introductionmentioning

confidence: 99%

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

2020

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Sentiment lexicon, which provides sentiment information for words, plays an important role in sentiment analysis task. Currently, most of sentiment lexicons have only one sentiment polarity for each word and ignore sentimental ambiguity. In this paper, we propose to construct the sentiment lexicon based on context-dependent part-of-speech (POS) chunks, namely CP-chunks, which aims at solving the ambiguity of lexical sentiments. Given that the POS of context has impact on the word polarity and intensity, we take CP-chunks as an unit to do sentiment calculation. Our method is evaluated through the classification task of text sentiment. The experiment results indicate that, in comparison with the existing methods, the applicability of our method is more stable and balanced for both the positive and negative polarities corpora, and the accuracy of our method reaches 82% for the sentiment classification of a domain-specific corpus.

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“…Inspired by the idea of concept space (Hori, 1997), we first build an accurate boundary for a given course to alleviate the semantic drift during candidate concept generation from an external knowledge base. Then we transform the expansion into a binary classification problem as previous positive unlabeled learning methods for set expansion (Li et al, 2010;Wang et al, 2017). Three types of features are proposed to incorporate heterogeneous information into classifier to identify high-quality concepts among candidates.…”

Section: Introductionmentioning

confidence: 99%

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game

Yu¹,

Wang²,

Luo³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of knowledge guidance, existing methods can not effectively expand course concepts in complex MOOC environments. In this paper, we first build a novel boundary during searching for new concepts via external knowledge base and then utilize heterogeneous features to verify the highquality results. In addition, to involve human efforts in our model, we design an interactive optimization mechanism based on a game. Our experiments on the four datasets from Coursera and XuetangX show that the proposed method achieves significant improvements(+0.19 by MAP) over existing methods. The source code and datasets 1 have been published.

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Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association

Cited by 43 publications

References 28 publications

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

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

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game

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