Machine Learning based Language Modelling of Code Switched Data

Kumar, Vaibhav; Pasari, Shubham; Patil, Vallabh Pravin; Seniaray, Sumedha

doi:10.1109/icesc48915.2020.9155695

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Another article used machine learning to assess Amazon product review sentiment across categories. Text Blob, Logistic Regression, SVM, and Multinomial Naive Bayes improved sentiment classification accuracy, proving that various review sentiment ratings may be predicted [56]. A study [57] focused on sentiment polarity analysis for e-commerce customer reviews, while [58] presented an EESNN-SA-OPR method utilizing Collaborative Filtering (CF) and product-to-product similarity.…”

Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

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: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

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

Malik,

Bilal

2024

Preprint

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“…The first EN-ZH LM was designed by Vu et al (2012) using statistical machine translation techniques. With deep learning, LMs were also trained directly on CS data (Kumar et al, 2020). Luo et al (2018) trained an endto-end EN-ZH CS speech recognition models, using a connectionist temporal classification and attention based model.…”

Section: Related Workmentioning

confidence: 99%

Intrinsic evaluation of language models for code-switching

Cheong¹,

Chieu²,

Lim³

2021

Proceedings of the Seventh Workshop on Noisy User-Generated Text (W-Nut 2021)

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Language models used in speech recognition are often either evaluated intrinsically using perplexity on test data, or extrinsically with an automatic speech recognition (ASR) system. The former evaluation does not always correlate well with ASR performance, while the latter could be specific to particular ASR systems. Recent work proposed to evaluate language models by using them to classify ground truth sentences among alternative phonetically similar sentences generated by a fine state transducer. Underlying such an evaluation is the assumption that the generated sentences are linguistically incorrect. In this paper, we first put this assumption into question, and observe that alternatively generated sentences could often be linguistically correct when they differ from the ground truth by only one edit. Secondly, we showed that by using multi-lingual BERT, we can achieve better performance than previous work on two codeswitching data sets. Our implementation is publicly available on Github. 1

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