An Opinion Mining Model for Generic Domains

Tuveri, Franco; Angioni, Manuela

doi:10.1007/978-3-642-40621-8_3

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Relational dependency parsers are used in Qiu et al (2011) to drop out irrelevant candidate aspect terms. The aspects extracted are mapped to their associated sentiments (Tuveri and Angioni 2014;Zhang and Liu 2014). Limited work has been done in finding implicit aspects which are complex to find as compared to explicit aspects.…”

Section: Frequency and Relation-based Techniquesmentioning

confidence: 99%

Lifelong aspect extraction from big data: knowledge engineering

Khan

Durrani

Khalid

et al. 2016

Complex Adapt Syst Model

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Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.

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Section: Frequency and Relation-based Techniquesmentioning

confidence: 99%

Lifelong aspect extraction from big data: knowledge engineering

Khan

Durrani

Khalid

et al. 2016

Complex Adapt Syst Model

View full text Add to dashboard Cite

show abstract

Predicting emotions in online social networks: challenges and opportunities

Alqahtani

Alothaim

2022

Multimed Tools Appl

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Framework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands

Park

2020

IEEE Access

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Cosmetics brand managers' efforts in monitoring customer satisfaction and service quality have suffered due to the lack of effective analysis methods. In order to derive more comprehensive and objective insights into customer opinions on product quality and preferences for cosmetics brands, this study derived an online-review-based process for evaluation of customer satisfaction. The present study developed a systematic approach to the evaluation of relative customer satisfaction with cosmetics brands via sentiment analysis and statistical data analysis, and interpreted the determinants of positive and negative opinions via Term Frequency-Inverse Document Frequency (TF-IDF) analysis. To illustrate the efficacy and applicability of the proposed approach, an empirical case study applying it to the global top 26 cosmetics brands was conducted, which evaluated relative customer satisfaction with brands and examined the main causes of positive and negative opinions. The proposed approach is expected to be employed by cosmetics companies to realize or improve satisfaction with the brands that customers evaluate. Furthermore, we hope that it can be used as a source of fundamental data that could be applied to efforts to improve both brand competitiveness and provision of systematic services. INDEX TERMS Sentiment analysis, text-mining, customer satisfaction, online reviews, cosmetics brand.

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An Opinion Mining Model for Generic Domains

Cited by 5 publications

References 6 publications

Lifelong aspect extraction from big data: knowledge engineering

Lifelong aspect extraction from big data: knowledge engineering

Predicting emotions in online social networks: challenges and opportunities

Framework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands

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