Analysis of customer reviews with an improved VADER lexicon classifier Article Swipe
Kousik Barik
,
Sanjay Misra
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s40537-023-00861-x
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s40537-023-00861-x
Background The importance of customer reviews in determining satisfaction has significantly increased in the digital marketplace. Using sentiment analysis in customer reviews has immense potential but encounters challenges owing to domain heterogeneity. The sentiment orientation of words varies by domain; however, comprehending domain-specific sentiment reviews remains a significant constraint. Aim This study proposes an Improved VADER (IVADER) lexicon-based classification model to evaluate customer sentiment in multiple domains. The model involves constructing a domain-specific dictionary based on the VADER lexicon and classifying doeviews using the constructed dictionary. Methodology The proposed IVADER model uses data preprocessing, Vectorizer transformation, WordnetLemmatizer-based feature selection, and enhanced VADER Lexicon classifier. Result Compared to existing studies, the IVVADER model accomplished outcomes of accuracy of 98.64%, precision of 97%, recall of 94%, f1-measure of 92%, and less training time of 44 s for classification. Outcome Product designers and business organizations can benefit from the IVADER model to evaluate multi-domain customer sentiment and introduce new products in the competitive online marketplace.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s40537-023-00861-x
- https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-023-00861-x
- OA Status
- gold
- Cited By
- 39
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390650744
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390650744Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s40537-023-00861-xDigital Object Identifier
- Title
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Analysis of customer reviews with an improved VADER lexicon classifierWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-07Full publication date if available
- Authors
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Kousik Barik, Sanjay MisraList of authors in order
- Landing page
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https://doi.org/10.1186/s40537-023-00861-xPublisher landing page
- PDF URL
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https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-023-00861-xDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-023-00861-xDirect OA link when available
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Lexicon, Computer science, Sentiment analysis, Artificial intelligence, Classifier (UML), Preprocessor, Domain (mathematical analysis), Natural language processing, Data pre-processing, Machine learning, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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39Total citation count in OpenAlex
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2025: 29, 2024: 10Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.98.64%, | 118 |
| abstract_inverted_index.IVVADER | 111 |
| abstract_inverted_index.Lexicon | 103 |
| abstract_inverted_index.Outcome | 137 |
| abstract_inverted_index.Product | 138 |
| abstract_inverted_index.benefit | 144 |
| abstract_inverted_index.digital | 15 |
| abstract_inverted_index.domain; | 40 |
| abstract_inverted_index.feature | 98 |
| abstract_inverted_index.immense | 24 |
| abstract_inverted_index.lexicon | 79 |
| abstract_inverted_index.remains | 46 |
| abstract_inverted_index.reviews | 6, 22, 45 |
| abstract_inverted_index.(IVADER) | 57 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Compared | 106 |
| abstract_inverted_index.Improved | 55 |
| abstract_inverted_index.accuracy | 116 |
| abstract_inverted_index.analysis | 19 |
| abstract_inverted_index.business | 141 |
| abstract_inverted_index.customer | 5, 21, 63, 152 |
| abstract_inverted_index.doeviews | 82 |
| abstract_inverted_index.domains. | 67 |
| abstract_inverted_index.enhanced | 101 |
| abstract_inverted_index.evaluate | 62, 150 |
| abstract_inverted_index.existing | 108 |
| abstract_inverted_index.however, | 41 |
| abstract_inverted_index.involves | 70 |
| abstract_inverted_index.multiple | 66 |
| abstract_inverted_index.outcomes | 114 |
| abstract_inverted_index.products | 157 |
| abstract_inverted_index.proposed | 89 |
| abstract_inverted_index.proposes | 53 |
| abstract_inverted_index.studies, | 109 |
| abstract_inverted_index.training | 130 |
| abstract_inverted_index.designers | 139 |
| abstract_inverted_index.increased | 12 |
| abstract_inverted_index.introduce | 155 |
| abstract_inverted_index.potential | 25 |
| abstract_inverted_index.precision | 119 |
| abstract_inverted_index.sentiment | 18, 34, 44, 64, 153 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Vectorizer | 95 |
| abstract_inverted_index.challenges | 28 |
| abstract_inverted_index.dictionary | 74 |
| abstract_inverted_index.encounters | 27 |
| abstract_inverted_index.f1-measure | 125 |
| abstract_inverted_index.importance | 3 |
| abstract_inverted_index.selection, | 99 |
| abstract_inverted_index.Methodology | 87 |
| abstract_inverted_index.classifier. | 104 |
| abstract_inverted_index.classifying | 81 |
| abstract_inverted_index.competitive | 160 |
| abstract_inverted_index.constraint. | 49 |
| abstract_inverted_index.constructed | 85 |
| abstract_inverted_index.determining | 8 |
| abstract_inverted_index.dictionary. | 86 |
| abstract_inverted_index.orientation | 35 |
| abstract_inverted_index.significant | 48 |
| abstract_inverted_index.accomplished | 113 |
| abstract_inverted_index.constructing | 71 |
| abstract_inverted_index.marketplace. | 16, 162 |
| abstract_inverted_index.multi-domain | 151 |
| abstract_inverted_index.satisfaction | 9 |
| abstract_inverted_index.comprehending | 42 |
| abstract_inverted_index.lexicon-based | 58 |
| abstract_inverted_index.organizations | 142 |
| abstract_inverted_index.significantly | 11 |
| abstract_inverted_index.classification | 59 |
| abstract_inverted_index.heterogeneity. | 32 |
| abstract_inverted_index.preprocessing, | 94 |
| abstract_inverted_index.classification. | 136 |
| abstract_inverted_index.domain-specific | 43, 73 |
| abstract_inverted_index.transformation, | 96 |
| abstract_inverted_index.WordnetLemmatizer-based | 97 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5064136287 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I3130438513 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.99430022 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |