Customer Sentiments in Product Reviews: A Comparative Study with GooglePaLM

Olamilekan Shobayo, Swethika Sasikumar, Sandhya Makkar, Obinna Okoyeigbo

Research output: Contribution to journalArticle (journal)peer-review

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Abstract

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.
Original languageEnglish
Pages (from-to)241-254
Number of pages14
JournalAnalytics
Volume3
Issue number2
Early online date18 Jun 2024
DOIs
Publication statusPublished - 18 Jun 2024

Keywords

  • sentiment analysis
  • natural language processing
  • GooglePaLM
  • product reviews
  • BERT
  • VADER
  • Emotion detection
  • large language models
  • emotion detection

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