Academic Journal
Customer Sentiments in Product Reviews: A Comparative Study with GooglePaLM
| العنوان: | Customer Sentiments in Product Reviews: A Comparative Study with GooglePaLM |
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| المؤلفون: | Olamilekan Shobayo, Swethika Sasikumar, Sandhya Makkar, Obinna Okoyeigbo |
| المصدر: | Analytics, Vol 3, Iss 2, Pp 241-254 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Probabilities. Mathematical statistics |
| مصطلحات موضوعية: | sentiment analysis, natural language processing, GooglePaLM, product reviews, BERT, VADER, Electronic computers. Computer science, QA75.5-76.95, Probabilities. Mathematical statistics, QA273-280 |
| الوصف: | In this work, we evaluated the efficacy of Google’s Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon’s fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2813-2203 |
| Relation: | https://www.mdpi.com/2813-2203/3/2/14; https://doaj.org/toc/2813-2203 |
| DOI: | 10.3390/analytics3020014 |
| URL الوصول: | https://doaj.org/article/d5b419dedb58404dbf533421108985f3 |
| رقم الانضمام: | edsdoj.5b419dedb58404dbf533421108985f3 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 28132203 |
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| DOI: | 10.3390/analytics3020014 |