What is it about?
This paper presents a data analytics methodology for benchmarking sentiment scoring algorithms in the analysis of customer reviews. The study focuses on sentiment analysis, which involves examining people's emotions and opinions about a particular subject. The research extends the existing literature by developing and demonstrating the applicability of the methodology using Amazon product reviews as the source data. By analyzing text-based content through text analytics and sentiment analysis, valuable insights can be gained for online retailers like Amazon. The study aims to examine the predictive power of machine learning algorithms in predicting scores and analyze patterns in the differences between scores obtained from different sentiment scoring algorithms. The methodology involves data preprocessing, text analysis, sentiment analysis using three different algorithms, model evaluation, and gap analysis. The findings can help improve understanding of customer needs and preferences, enhance product development, and inform decision-making in the e-commerce industry.
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Why is it important?
The research is important for several reasons. Firstly, with the increasing digitalization and user-generated content on the internet, sentiment analysis has become crucial in understanding people's opinions and emotions. This study extends the existing literature by developing a methodology specifically for benchmarking sentiment scoring algorithms in the context of customer reviews. The uniqueness of this work lies in its focus on online customer reviews, particularly using Amazon product reviews as the source data. By analyzing text-based content through text analytics and sentiment analysis, valuable insights can be gained for online retailers like Amazon to understand customer sentiments and improve their products or services accordingly. The research also contributes by examining the predictive power of machine learning algorithms in predicting sentiment scores and analyzing patterns in the differences between scores obtained from different sentiment scoring algorithms. This can help researchers and practitioners in the field of sentiment analysis to evaluate and compare the performance of different algorithms, leading to advancements in the development of more accurate and reliable sentiment analysis tools. Overall, this research provides a valuable methodology for benchmarking sentiment scoring algorithms in the analysis of customer reviews, offering insights that can benefit both researchers and practitioners in the field of sentiment analysis and online retail.
Perspectives
The presented research opens up fresh perspectives and new opportunities for researchers and practitioners in the field of sentiment analysis. It provides a comprehensive methodology for evaluating and comparing sentiment scoring algorithms, specifically in the context of customer reviews. This work can motivate further research studies in several areas. Firstly, it can inspire researchers to explore and develop more advanced sentiment scoring algorithms that can accurately analyze and interpret customer sentiments. Secondly, it can encourage the development of improved benchmarking techniques to assess the performance of sentiment analysis algorithms. Additionally, this research can motivate studies on the application of sentiment analysis in other domains beyond customer reviews, such as social media, healthcare, or finance. Overall, this publication contributes to the advancement of sentiment analysis research and provides a foundation for future investigations in the field.
Gurdal Ertek
Read the Original
This page is a summary of: A Data Analytics Methodology for Benchmarking of Sentiment Scoring Algorithms in the Analysis of Customer Reviews, January 2023, Springer Science + Business Media,
DOI: 10.1007/978-981-99-3243-6_46.
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