An Ontology-Based Approach to Sentiment Classification of Mixed Opinions in Online Restaurant Reviews

Kim, Hea-Jin; Song, Min

doi:10.1007/978-3-319-03260-3_9

Cited by 4 publications

(10 citation statements)

References 30 publications

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“…Therefore, we investigate the combination of SW technologies with bias mitigation methods. Bias mitigation has generally been divided into three groups [56,62]: those focusing on changing the training data [2,6,19,21,24,39,41,46,47,57,85], the learning algorithm during the model generation [3,31,51,54,76,88], or the model outcomes according to the results in a holdout dataset which was not involved during the training phase [29]. Such methods may mitigate undesirable associations of specific demographic groups with hateful connotations.…”

Section: Semanticsmentioning

confidence: 99%

“…In machine learning (ML), the absence of the context about the domain of the text has shown to leave annotators in an indecisive state so that their annotations incorrectly shift towards the most frequent sense of a word [17]. The use of subjective text and opinions or any data arising from human interpretation can also have challenging impacts if used to develop AI applications [3,31,46,51].…”

Section: Bias In Aimentioning

confidence: 99%

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Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Lobo

Daga

Alani

et al. 2023

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Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges.

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Section: Semanticsmentioning

confidence: 99%

Section: Bias In Aimentioning

confidence: 99%

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Lobo

Daga

Alani

et al. 2023

View full text Add to dashboard Cite

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“…Some other researches such as [5,6,8,16] have been focused on the ontology of the product or the main topic of the review in order to determine the sentiment orientation. The work in [8] proposes a combined domain ontology based SOA where supervised learning technique is used to extract features and opinions from the movie reviews to enhance the existing sentiment classification tasks.…”

Section: Ontology-based Sentiment Orientation Approachmentioning

confidence: 99%

“…In opinion mining, sentiment analysis and classification of product reviews are a common problem and varieties of Sentiment Orientation Approaches SOA have been used to address the problem. Some approaches such as [1,2,3,4] are based on lexical resources, others such as [5,6] are based on the product's features ontology and papers such as [3,7,8] combine element of both. Product Ontology and lexicon base classifiers are unable to capture either implicit features of the product or opinion idiomatic expressions although it is common to find these expressions in product's reviews.…”

Section: Introductionmentioning

confidence: 99%

CIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in E-commerce

Rahimi¹,

Bakkali²

2015

Advances in Intelligent Systems and Computing

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Due to the abundant amount of Customer's Reviews available in E-commerce platforms, Trust Reputation Systems remain reliable means to determine, circulate and restore the credibility and reputation of reviewers and their provided reviews. In fact before starting the process of Reputation score's calculation, we need to develop an accurate Sentiment orientation System able to extract opinion expressions, analyze them and determine the sentiment orientation of the Review and then classify it into positive, negative and objective. In this paper, we propose a novel semi-supervised approach which is a Combined Idiomatic-Ontology based Sentiment Orientation System (CIOSOS) that realizes a domain-dependent sentiment analysis of reviews. The main contribution of the system is to expand the general opinion lexicon SentiWordNet to a custom-made opinion lexicon (SentiWordNet++) with domain-dependent "opinion indicators" as well as "idiomatic expressions". The system relies also on a semi-supervised learning method that uses the general lexicon WordNet to identify synonyms or antonyms of the expanded terms and get their polarities from SentiWordNet and then store them in SentiWordNet++. The Sentiment polarity and the classification of the review provided by the CIOSOS is used as an input of our Reputation Algorithm proposed in previous papers in order to generate the Reputation score of the reviewer. We also provide an improvement in calculation method used to generate a "granular" reputation score of a feature or subfeature of the product.

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“…The document with simple expression of sentiment is more likely to be classified correctly as people tend to use short words and use symbols in their comments to express their opinion (Khan 2011). If a document has both positive and negative words, the polarity score of the document may not be good enough for classification as the score is usually derived from the summation of scores of each individual word (Kim and Song 2013).…”

Section: Selection Of Data Properties For Comparisonmentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

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An Ontology-Based Approach to Sentiment Classification of Mixed Opinions in Online Restaurant Reviews

Cited by 4 publications

References 30 publications

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

CIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in E-commerce

Data properties and the performance of sentiment classification for electronic commerce applications

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