Cite this DOI

Lohia, D. H. (2024). Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis. *Journal of Science & Technology*, *9*(1), 139–147. https://doi.org/10.46243/jst.2024.v9.i01.pp139-147

@article{lohia2024identifying,
  author    = {Lohia, Dr. Harsh},
  title     = {{Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis}},
  journal   = {Journal of Science \& Technology},
  year      = {2024},
  month     = {jan},
  volume    = {9},
  number    = {1},
  pages     = {139--147},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2024.v9.i01.pp139-147},
  url       = {https://doi.org/10.46243/jst.2024.v9.i01.pp139-147},
  language  = {en},
  abstract  = {Dual Sentiment Analysis has emerged as a crucial and active research field. It involves extracting sentiment from comments, feedback, or critiques, which serves as valuable indicators for various purposes. To address this, we propose a novel dual training algorithm that utilizes both original and reversed training reviews to develop a robust sentiment classifier. Additionally, we introduce a dual prediction algorithm that comprehensively assesses both aspects of a review for classification during testing. The proposed approach goes beyond traditional polarity (positive-negative) classification by extending the framework to a 3-class system, which includes neutral reviews. This enhancement allows for a more nuanced understanding of sentiment. By considering neutral reviews, we gain deeper insights into the sentiment landscape. Dual Sentiment Analysis plays a pivotal role in helping companies gauge the level of acceptance of their products and formulate strategies to improve product quality. Moreover, it empowers policymakers and politicians to gain valuable insights by analyzing public sentiments on policies, public services, and political issues.}
}

TY  - JOUR
TI  - Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis
AU  - Lohia, Dr. Harsh
JO  - Journal of Science & Technology
PY  - 2024
DA  - 2024/01/25/
VL  - 9
IS  - 1
SP  - 139
EP  - 147
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Dual Sentiment Analysis has emerged as a crucial and active research field. It involves extracting sentiment from comments, feedback, or critiques, which serves as valuable indicators for various purposes. To address this, we propose a novel dual training algorithm that utilizes both original and reversed training reviews to develop a robust sentiment classifier. Additionally, we introduce a dual prediction algorithm that comprehensively assesses both aspects of a review for classification during testing. The proposed approach goes beyond traditional polarity (positive-negative) classification by extending the framework to a 3-class system, which includes neutral reviews. This enhancement allows for a more nuanced understanding of sentiment. By considering neutral reviews, we gain deeper insights into the sentiment landscape. Dual Sentiment Analysis plays a pivotal role in helping companies gauge the level of acceptance of their products and formulate strategies to improve product quality. Moreover, it empowers policymakers and politicians to gain valuable insights by analyzing public sentiments on policies, public services, and political issues.
DO  - 10.46243/jst.2024.v9.i01.pp139-147
UR  - https://doi.org/10.46243/jst.2024.v9.i01.pp139-147
ER  -

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    "URL": "https://doi.org/10.46243/jst.2024.v9.i01.pp139-147",
    "title": "Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis",
    "source": "Smart Scholars DOI Registry",
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    "language": "en",
    "abstract": "Dual Sentiment Analysis has emerged as a crucial and active research field. It involves extracting sentiment from comments, feedback, or critiques, which serves as valuable indicators for various purposes. To address this, we propose a novel dual training algorithm that utilizes both original and reversed training reviews to develop a robust sentiment classifier. Additionally, we introduce a dual prediction algorithm that comprehensively assesses both aspects of a review for classification during testing. The proposed approach goes beyond traditional polarity (positive-negative) classification by extending the framework to a 3-class system, which includes neutral reviews. This enhancement allows for a more nuanced understanding of sentiment. By considering neutral reviews, we gain deeper insights into the sentiment landscape. Dual Sentiment Analysis plays a pivotal role in helping companies gauge the level of acceptance of their products and formulate strategies to improve product quality. Moreover, it empowers policymakers and politicians to gain valuable insights by analyzing public sentiments on policies, public services, and political issues.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2024.v9.i01.pp139-147 gives all four in one JSON answer.