Survey on Ontology-Based Sentiment Analysis of Customer Reviews for Products and Services

Bandari, Sumalatha D.; Vardhan, B. Vishnu

doi:10.1007/978-981-15-1097-7_8

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…As a medium-sized collection of domain-specific texts, Kickstarter was chosen as a data source. 4 Kickstarter, a popular source for data scientists, includes approximately 480K campaign descriptions 5 in the form of hypertexts, including text, images, videos, and hyperlinks. 6 To identify the domains of interest of each campaign, we leveraged the labels available on the Kickstarter platform to categorize each campaign description.…”

Section: Mining Of Domain Corporamentioning

confidence: 99%

“…occ(w 1 , t) > 0 and occ(w 2 , t) > 0 , we computed the number of co-occurrences co_occ(w 1 , w 2 , t) of words w 1 and w 2 in the description t as co_occ(w 1 , w 2 , t) = occ(w 1 , t) * occ(w 2 , t); -Step 2.4: Since Kickstarter campaigns are laJbeled with two domain categories (i.e., a main category and an optional subcategory), we leveraged this labeling to compute the distributions of occurrences and co-occurrences of concepts across domains. Excerpt of the DomainSenticNet concept "apple" 4 Monthly updated dataset of the Kickstarter campaign URLs is available at: https ://webro bots.io/kicks tarte r-datas ets/ 5 Real-time statistics are accessible at: https ://www.kicks tarte r.com/ help/stats 6 We were able to crawl a total of ∼230K Kickstarter descriptions from the original ∼480K campaigns. 7 An overview of the respective domains and related statistics is available at: https ://www.kicks tarte r.com/help/stats Returning to the "apple" concept example, Fig.…”

Section: Mining Of Domain Corporamentioning

confidence: 99%

“…The availability of the above-mentioned resources is beneficial for all ontology-driven sentiment analysis (ODSA)based applications. Specifically, the authors of [4] recently surveyed studies applying ODSA to customer reviews. Furthermore, as an example of an ODSA-based approach, the authors of [25] presented a hybrid solution for sentence-level ABSA using a lexicalized domain ontology in combination with neural attention networks.…”

Section: Related Workmentioning

confidence: 99%

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DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

et al. 2021

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In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.

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Section: Mining Of Domain Corporamentioning

confidence: 99%

Section: Mining Of Domain Corporamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

et al. 2021

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“…Various approaches for lexicon-based word polarity identification have been developed in recent years to analyze emotions, attitudes, and opinions about particular objects or events [30,31,32]. These methods can be generally classified into two groups: corpus-based [48,49,50,51] and dictionary-based [45,46,47,52,53].…”

Section: Related Workmentioning

confidence: 99%

“…Various polarity identification methods have been proposed in literature to determine and classify the sentiment orientation in a document-level text [16,17,18], sentencelevel text [19], and word, or feature level [20]. These methods can be broadly categorized as belonging to one of the two approaches: machine learning-based methods [21,22,23,24,25] and lexicon-based methods [26,27,28,29,30,31,32]. Machine learning-based methods use a supervised learning mechanism to motivate a classifier from a textual collection of training data containing of a set of manual sentiment labels.…”

Section: Introductionmentioning

confidence: 99%

Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

Abdalgader

Shibli

2020

IEEE Access

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Determining the polarities of words in a given context has been in existence since the inception of computational linguistics, text mining, and sentiment analysis. Due to its fundamental role in determining the overall semantic orientation of natural language expressions, it is considered one of the most challenging issues facing these areas of research. This paper introduces a new implementation of the lexicon-based word polarity identification method on several customer reviews datasets. Herein, we use a variation of a lexiconbased word polarity identification method that operates by computing the semantic relatedness between the context expansion set of the target word and a synonym expansion set comprising the synonyms of all words surrounding the target word within the original text fragment. The polarity of the target word is determined as that for which the semantic relatedness between these two meaningful sets is the highest. Unlike most existing lexicon-based multi-polarity word identification methods, the used method is not based on estimating pairwise relatedness at term-level, but instead, it is based on measuring semantic relatedness at the fragmentlevel. This enables the exploration and capture of a higher degree of semantic and sentimental information, and is more consistent with people' understanding through the consideration of the larger context in which the word appears. Its performance can be further improved by incorporating an initial step in which the relative negation scope of words in the given text fragment is managed while determining their sentiment orientation. The implementation results demonstrate that the used variation of the lexicon-based word polarity identification method performs favourably against compared methods, as evaluated on numerous benchmark datasets through stand-alone and end-to-end evaluation models.

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An adaptable scheme to enhance the sentiment classification of Telugu language

Naik

Vasumathi

Kumar

2021

Soc. Netw. Anal. Min.

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Survey on Ontology-Based Sentiment Analysis of Customer Reviews for Products and Services

Cited by 7 publications

References 19 publications

DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

An adaptable scheme to enhance the sentiment classification of Telugu language

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