Ontology-Based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning Architectures Article Swipe
Deepak Kumar Jain
,
Shamimul Qamar
,
Saurabh Raj Sangwan
,
Weiping Ding
,
Anand J. Kulkarni
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3624012
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3624012
When tested with popular datasets, sentiment categorization using deep learning (DL) algorithms will produce positive results. Building a corpus on novel themes to train machine learning methods in sentiment classification with high assurance, however, will be difficult. This study proposes a way for representing efficient features of a dataset into a word embedding layer of DL methods in sentiment classification known as KPRO (knowledge processing and representation based on ontology), a procedure to embed knowledge in the ontology of opinion datasets. This research proposes novel methods in ontology-based natural language processing utilizing feature extraction as well as classification by a DL technique. Here, input text has been taken as web ontology based text and is processed for word embedding. Then the feature mapping is carried out for this processed text using least square mapping in which the sentiment-based text has been mapped for feature extraction. The feature extraction is carried out using a Markov model based auto-feature encoder (MarMod_AuFeaEnCod). Extracted features are classified by utilizing hierarchical convolutional attention networks. Based on this classified output, the sentiment of the text obtained from web data has been analyzed. Results are carried out for Twitter and Facebook ontology-based sentimental analysis datasets in terms of accuracy, precision, recall, F-1 score, RMSE, and loss curve analysis. For the Twitter dataset, the proposed MarMod_AuFeaEnCod_HCAN attains an accuracy of 98%, precision of 95%, recall of 93%, F-1 score of 91%, RMSE of 88%, and loss curve of 70.2%. For Facebook, ontology web dataset analysis is also carried out with the same parameters in which the proposed MarMod_AuFeaEnCod_HCAN acquires accuracy of 96%, precision of 92%, recall of 94%, F-1 score of 91%, RMSE of 77%, and loss curve of 68.2%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3624012
- https://dl.acm.org/doi/pdf/10.1145/3624012
- OA Status
- bronze
- Cited By
- 8
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388658829
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388658829Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3624012Digital Object Identifier
- Title
-
Ontology-Based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning ArchitecturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-14Full publication date if available
- Authors
-
Deepak Kumar Jain, Shamimul Qamar, Saurabh Raj Sangwan, Weiping Ding, Anand J. KulkarniList of authors in order
- Landing page
-
https://doi.org/10.1145/3624012Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3624012Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://dl.acm.org/doi/pdf/10.1145/3624012Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Sentiment analysis, Ontology, Natural language processing, Word embedding, Feature (linguistics), Feature extraction, Categorization, Information retrieval, Embedding, Machine learning, Philosophy, Linguistics, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | ACM Transactions on Asian and Low-Resource Language Information Processing |
| primary_location.landing_page_url | https://doi.org/10.1145/3624012 |
| publication_date | 2023-11-14 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2919350184, https://openalex.org/W2999719588, https://openalex.org/W3020334141, https://openalex.org/W2810258172, https://openalex.org/W3036066542, https://openalex.org/W4283777872, https://openalex.org/W3209875211, https://openalex.org/W3156904194, https://openalex.org/W3143134197, https://openalex.org/W3003681195, https://openalex.org/W4200260807, https://openalex.org/W4220883868, https://openalex.org/W3203412535, https://openalex.org/W2995912770, https://openalex.org/W4308325149 |
| referenced_works_count | 15 |
| abstract_inverted_index.a | 17, 40, 47, 50, 70, 99, 152 |
| abstract_inverted_index.DL | 55, 100 |
| abstract_inverted_index.an | 219 |
| abstract_inverted_index.as | 61, 94, 96, 108 |
| abstract_inverted_index.be | 35 |
| abstract_inverted_index.by | 98, 163 |
| abstract_inverted_index.in | 27, 57, 75, 86, 134, 198, 255 |
| abstract_inverted_index.is | 114, 123, 148, 247 |
| abstract_inverted_index.of | 46, 54, 78, 176, 200, 221, 224, 227, 231, 234, 239, 262, 265, 268, 272, 275, 280 |
| abstract_inverted_index.on | 19, 68, 170 |
| abstract_inverted_index.to | 22, 72 |
| abstract_inverted_index.F-1 | 204, 229, 270 |
| abstract_inverted_index.For | 211, 241 |
| abstract_inverted_index.The | 145 |
| abstract_inverted_index.and | 65, 113, 192, 207, 236, 277 |
| abstract_inverted_index.are | 161, 187 |
| abstract_inverted_index.for | 42, 116, 126, 142, 190 |
| abstract_inverted_index.has | 105, 139, 183 |
| abstract_inverted_index.out | 125, 150, 189, 250 |
| abstract_inverted_index.the | 76, 120, 136, 174, 177, 212, 215, 252, 257 |
| abstract_inverted_index.way | 41 |
| abstract_inverted_index.web | 109, 181, 244 |
| abstract_inverted_index.(DL) | 10 |
| abstract_inverted_index.77%, | 276 |
| abstract_inverted_index.88%, | 235 |
| abstract_inverted_index.91%, | 232, 273 |
| abstract_inverted_index.92%, | 266 |
| abstract_inverted_index.93%, | 228 |
| abstract_inverted_index.94%, | 269 |
| abstract_inverted_index.95%, | 225 |
| abstract_inverted_index.96%, | 263 |
| abstract_inverted_index.98%, | 222 |
| abstract_inverted_index.KPRO | 62 |
| abstract_inverted_index.RMSE | 233, 274 |
| abstract_inverted_index.Then | 119 |
| abstract_inverted_index.This | 37, 81 |
| abstract_inverted_index.When | 0 |
| abstract_inverted_index.also | 248 |
| abstract_inverted_index.been | 106, 140, 184 |
| abstract_inverted_index.data | 182 |
| abstract_inverted_index.deep | 8 |
| abstract_inverted_index.from | 180 |
| abstract_inverted_index.high | 31 |
| abstract_inverted_index.into | 49 |
| abstract_inverted_index.loss | 208, 237, 278 |
| abstract_inverted_index.same | 253 |
| abstract_inverted_index.text | 104, 112, 129, 138, 178 |
| abstract_inverted_index.this | 127, 171 |
| abstract_inverted_index.well | 95 |
| abstract_inverted_index.will | 12, 34 |
| abstract_inverted_index.with | 2, 30, 251 |
| abstract_inverted_index.word | 51, 117 |
| abstract_inverted_index.Based | 169 |
| abstract_inverted_index.Here, | 102 |
| abstract_inverted_index.RMSE, | 206 |
| abstract_inverted_index.based | 67, 111, 155 |
| abstract_inverted_index.curve | 209, 238, 279 |
| abstract_inverted_index.embed | 73 |
| abstract_inverted_index.input | 103 |
| abstract_inverted_index.known | 60 |
| abstract_inverted_index.layer | 53 |
| abstract_inverted_index.least | 131 |
| abstract_inverted_index.model | 154 |
| abstract_inverted_index.novel | 20, 84 |
| abstract_inverted_index.score | 230, 271 |
| abstract_inverted_index.study | 38 |
| abstract_inverted_index.taken | 107 |
| abstract_inverted_index.terms | 199 |
| abstract_inverted_index.train | 23 |
| abstract_inverted_index.using | 7, 130, 151 |
| abstract_inverted_index.which | 135, 256 |
| abstract_inverted_index.68.2%. | 281 |
| abstract_inverted_index.70.2%. | 240 |
| abstract_inverted_index.Markov | 153 |
| abstract_inverted_index.corpus | 18 |
| abstract_inverted_index.mapped | 141 |
| abstract_inverted_index.recall | 226, 267 |
| abstract_inverted_index.score, | 205 |
| abstract_inverted_index.square | 132 |
| abstract_inverted_index.tested | 1 |
| abstract_inverted_index.themes | 21 |
| abstract_inverted_index.Results | 186 |
| abstract_inverted_index.Twitter | 191, 213 |
| abstract_inverted_index.attains | 218 |
| abstract_inverted_index.carried | 124, 149, 188, 249 |
| abstract_inverted_index.dataset | 48, 245 |
| abstract_inverted_index.encoder | 157 |
| abstract_inverted_index.feature | 92, 121, 143, 146 |
| abstract_inverted_index.machine | 24 |
| abstract_inverted_index.mapping | 122, 133 |
| abstract_inverted_index.methods | 26, 56, 85 |
| abstract_inverted_index.natural | 88 |
| abstract_inverted_index.opinion | 79 |
| abstract_inverted_index.output, | 173 |
| abstract_inverted_index.popular | 3 |
| abstract_inverted_index.produce | 13 |
| abstract_inverted_index.recall, | 203 |
| abstract_inverted_index.Building | 16 |
| abstract_inverted_index.Facebook | 193 |
| abstract_inverted_index.accuracy | 220, 261 |
| abstract_inverted_index.acquires | 260 |
| abstract_inverted_index.analysis | 196, 246 |
| abstract_inverted_index.dataset, | 214 |
| abstract_inverted_index.datasets | 197 |
| abstract_inverted_index.features | 45, 160 |
| abstract_inverted_index.however, | 33 |
| abstract_inverted_index.language | 89 |
| abstract_inverted_index.learning | 9, 25 |
| abstract_inverted_index.obtained | 179 |
| abstract_inverted_index.ontology | 77, 110, 243 |
| abstract_inverted_index.positive | 14 |
| abstract_inverted_index.proposed | 216, 258 |
| abstract_inverted_index.proposes | 39, 83 |
| abstract_inverted_index.research | 82 |
| abstract_inverted_index.results. | 15 |
| abstract_inverted_index.Extracted | 159 |
| abstract_inverted_index.Facebook, | 242 |
| abstract_inverted_index.accuracy, | 201 |
| abstract_inverted_index.analysis. | 210 |
| abstract_inverted_index.analyzed. | 185 |
| abstract_inverted_index.attention | 167 |
| abstract_inverted_index.datasets, | 4 |
| abstract_inverted_index.datasets. | 80 |
| abstract_inverted_index.efficient | 44 |
| abstract_inverted_index.embedding | 52 |
| abstract_inverted_index.knowledge | 74 |
| abstract_inverted_index.networks. | 168 |
| abstract_inverted_index.precision | 223, 264 |
| abstract_inverted_index.procedure | 71 |
| abstract_inverted_index.processed | 115, 128 |
| abstract_inverted_index.sentiment | 5, 28, 58, 175 |
| abstract_inverted_index.utilizing | 91, 164 |
| abstract_inverted_index.(knowledge | 63 |
| abstract_inverted_index.algorithms | 11 |
| abstract_inverted_index.assurance, | 32 |
| abstract_inverted_index.classified | 162, 172 |
| abstract_inverted_index.difficult. | 36 |
| abstract_inverted_index.embedding. | 118 |
| abstract_inverted_index.extraction | 93, 147 |
| abstract_inverted_index.ontology), | 69 |
| abstract_inverted_index.parameters | 254 |
| abstract_inverted_index.precision, | 202 |
| abstract_inverted_index.processing | 64, 90 |
| abstract_inverted_index.technique. | 101 |
| abstract_inverted_index.extraction. | 144 |
| abstract_inverted_index.sentimental | 195 |
| abstract_inverted_index.auto-feature | 156 |
| abstract_inverted_index.hierarchical | 165 |
| abstract_inverted_index.representing | 43 |
| abstract_inverted_index.convolutional | 166 |
| abstract_inverted_index.categorization | 6 |
| abstract_inverted_index.classification | 29, 59, 97 |
| abstract_inverted_index.ontology-based | 87, 194 |
| abstract_inverted_index.representation | 66 |
| abstract_inverted_index.sentiment-based | 137 |
| abstract_inverted_index.(MarMod_AuFeaEnCod). | 158 |
| abstract_inverted_index.MarMod_AuFeaEnCod_HCAN | 217, 259 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.8729782 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |