Classifying insincere questions on Question Answering (QA) websites: meta-textual features and word embedding

Al-Ramahi, Mohammad; Alsmadi, Izzat

doi:10.1080/2573234x.2021.1895681

Cited by 2 publications

(2 citation statements)

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“…The experimental evaluation shows that the fastText embedding approach achieves the F1-socre of 91.00% (Shahzad et al 2019 ). Extracting meta-textual features and word-level features using the BERT approach gains an accuracy of 95% for classifying insincere questions on question-answering websites (Al-Ramahi and Alsmadi 2021 ). CNN with the Word2Vec model achieves an accuracy of 90% for text classification tasks (Kim and Hong 2021 ), (Ochodek et al 2020 ).…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

“… Dogru et al ( 2021 ) Text classification Turkish Text Classification 3600 (TTC-3600) dataset, BBC-News dataset CNN Doc2Vec CNN with Doc2Vec model achieves an accuracy of 94.17% 31. Al-Ramahi and Alsmadi ( 2021 ) Question–Answer classification Quora website, Dataset of Wikipedia comments Meta and word-level analysis Word2Vec, GloVe, fastText, BERT, TFIDF Classification using BERT achieves an accuracy of 95% 32. Roy et al ( 2020 ) SMS text classification UCI repository, SMS corpus NB, RF, GB, SGD, LSTM, CNN GloVe CNN + GloVe achieves an accuracy of 99.44% 33.…”

Section: Appendix Amentioning

confidence: 99%

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Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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