Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making

• URL: http://arxiv.org/abs/2411.00796v1
• Date: Fri, 18 Oct 2024 22:46:27 GMT
• Title: Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making
• Authors: Xinli Guo,
• Abstract summary: We leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. We employ transformer-based models, RoBERTa, to derive sentiment scores that accurately reflect the emotional tones of the reviews.
• Score: 0.0
• License:
• Abstract: In this study, we leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. By employing transformer-based models, RoBERTa, we analyze a vast dataset to derive sentiment scores that accurately reflect the emotional tones of the reviews. We provide an in-depth explanation of the underlying principles of these models and evaluate their performance in generating sentiment scores. Further, we conduct comprehensive data analysis and visualization to identify patterns and trends in sentiment scores, examining their alignment with behavioral economics principles such as electronic word of mouth (eWOM), consumer emotional reactions, and the confirmation bias. Our findings demonstrate the efficacy of advanced NLP models in sentiment analysis and offer valuable insights into consumer behavior, with implications for strategic decision-making and marketing practices.

Related papers

• Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews [2.0143010051030417]
  Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP) Traditional sentiment analysis methods, while useful for determining overall sentiment, often miss the implicit opinions about particular product or service features. This paper presents a comprehensive review of the evolution of ABSA methodologies, from lexicon-based approaches to machine learning.
  arXiv  Detail & Related papers  (2024-08-23T16:31:07Z)
• Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis [0.764671395172401]
  We evaluate sentiment analysis methods across a corpus of 58,000 comments on Reddit. Our research expands the scope by evaluating a diverse array of models. Our findings reveal that the RoBERTa model consistently outperforms the baseline models.
  arXiv  Detail & Related papers  (2024-05-27T03:59:28Z)
• Self-training Strategies for Sentiment Analysis: An Empirical Study [7.416913210816592]
  Self-training is an economical and efficient technique for developing sentiment analysis models. We compare several self-training strategies with the intervention of large language models.
  arXiv  Detail & Related papers  (2023-09-15T21:42:46Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews [7.897859138153238]
  We propose Painsight, an unsupervised framework for extracting dissatisfaction factors from customer reviews. Painsight employs pre-trained language models to construct sentiment analysis and topic models. It successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type.
  arXiv  Detail & Related papers  (2023-06-03T07:51:57Z)
• On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training [109.9218185711916]
  Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind social media texts or reviews. We propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
  arXiv  Detail & Related papers  (2023-04-19T11:07:43Z)
• Causal Intervention Improves Implicit Sentiment Analysis [67.43379729099121]
  We propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV) We first review sentiment analysis from a causal perspective and analyze the confounders existing in this task. Then, we introduce an instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment.
  arXiv  Detail & Related papers  (2022-08-19T13:17:57Z)
• Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa [23.00810941211685]
  Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis. Recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem.
  arXiv  Detail & Related papers  (2022-07-06T03:50:31Z)
• SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation [48.1799451277808]
  We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
  arXiv  Detail & Related papers  (2021-08-18T08:04:38Z)
• Sentiment Analysis Based on Deep Learning: A Comparative Study [69.09570726777817]
  The study of public opinion can provide us with valuable information. The efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems.
  arXiv  Detail & Related papers  (2020-06-05T16:28:10Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.