Irony Detection Based on Character Language Model Classifiers

Quintero, Yisel Clavel; García, Leticia Arco

doi:10.1007/978-3-030-01132-1_18

Cited by 1 publication

(2 citation statements)

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“…The challenge of navigating through verbose customer reviews was addressed through a multicriteria decision-making approach to recommend optimal products on platforms like Flipkart and Amazon [127]. The detection of ironic opinions in social networks and e-commerce was explored, comparing feature-based irony detection with a novel approach using character language model classifiers, showing competitive accuracy in experiments [128]. The evolving landscape of consumer behavior in e-commerce was examined, proposing an algorithmic solution to mitigate inaccuracies in user-generated reviews and enhance the decision-making process using NLP techniques [129].…”

Section: Marketing and Brand Managementmentioning

confidence: 99%

“…Researchers in [53] introduces a method for spotting sarcastic thoughts in online communication, emphasizing the necessity for novel methods to capture subtle language subtleties. [128] employs character language model classifiers to identify irony in social media and e-commerce communications. The study shows how difficult it is to spot irony in user-generated content on Twitter and e-commerce sites.…”

Section: Dealing With Sarcasm and Ironymentioning

confidence: 99%

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

Malik,

Bilal

2024

Preprint

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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.

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Section: Marketing and Brand Managementmentioning

confidence: 99%

Section: Dealing With Sarcasm and Ironymentioning

confidence: 99%