Using Online Customer Reviews to Classify, Predict, and Learn About Domestic Robot Failures

Honig, Shanee; Bartal, Alon; Parmet, Yisrael; Oron-Gilad, Tal

doi:10.1007/s12369-022-00929-3

Cited by 6 publications

(3 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[147] Amazon Challenged the assumption that longer product reviews are universally more helpful. [148] Amazon Investigated domestic robot failures using reviews. [149] TripAdvisor Investigated the Memorable Tourist Experience (MTE) concept through reviews.…”

Section: Ref Yearmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

View full text Add to dashboard Cite

In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

show abstract

Section: Ref Yearmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Technical failures, particularly related to Task Completion and Robustness, significantly impacted customer experience more than Interaction or Service failures. An NLP model predicted failure content in reviews, providing insights for prioritizing crucial issues for robotic system improvement [148]. Using TripAdvisor reviews, the study explored the Memorable Tourist Experience (MTE) concept, employing NLP and machine learning to analyze terms and relationships.…”

Section: Marketing and Brand Managementmentioning

confidence: 99%

“…[147] Amazon Challenged the assumption that longer product reviews are universally more helpful. [148] Amazon Investigated domestic robot failures using reviews.…”

Section: Semeval-2018mentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

View full text Add to dashboard Cite

In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

show abstract

Why are You Blinking at Me? Exploring Users’ Understanding of Robotic Status Indicators

Liberman-Pincu,

Honig,

Oron-Gilad

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Using Online Customer Reviews to Classify, Predict, and Learn About Domestic Robot Failures

Cited by 6 publications

References 63 publications

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Why are You Blinking at Me? Exploring Users’ Understanding of Robotic Status Indicators

Contact Info

Product

Resources

About