The impact of text preprocessing on the prediction of review ratings

Işik, Muhittin; Dağ, Hasan

doi:10.3906/elk-1907-46

Cited by 28 publications

(27 citation statements)

References 11 publications

(13 reference statements)

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“…Many works in the literature investigate the effect of preprocessing in machine learning classifiers 48 and the Turkish text classification 1,22,23,49 …”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Many works in the literature investigate the effect of preprocessing in machine learning classifiers 48 and the Turkish text classification. 1,22,23,49 Although we focus on the Turkish text classification, we realize that selected dataset contains some bilingual data, that is, Turkish and English words, when we examine the raw data. In real-world applications, bilingual data are quite a frequent case, especially in news datasets.…”

Section: Preprocessing For Supervised Machine Learning Algorithmsmentioning

confidence: 99%

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Improving automated Turkish text classification with learning‐based algorithms

Köksal

Yılmaz

2022

Concurrency and Computation

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Text classification is the process of determining categories or tags of a document depending on its content. Although text classification is a well‐known process, it has many steps that require tuning to improve mathematical models. This article provides a novel methodology and expresses key points to improve text classification performance using learning‐based algorithms and techniques. First, to check the effectiveness of the proposed methodology, we selected two public Turkish news benchmarking datasets. Then, we performed extensive testing using both supervised machine learning algorithms and state‐of‐art pre‐trained language models. The experimental results show that our methodology outperforms previous news classification studies on these benchmarking datasets improving categorization results based on F1‐score. Therefore, we conclude that the presented methodology efficiently improves the classification results and selects the feasible classifier for a given dataset.

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“…Many works in the literature investigate the effect of preprocessing in machine learning classifiers 48 and the Turkish text classification 1,22,23,49 …”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Preprocessing For Supervised Machine Learning Algorithmsmentioning

confidence: 99%

Improving automated Turkish text classification with learning‐based algorithms

Köksal

Yılmaz

2022

Concurrency and Computation

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“…Noise removal is used to remove punctuation marks and non-printable characters, such as [!"#$%&'()*+,-./:;ó? @\^_`{|}~] [34]. In this study, no process was carried out to remove the remaining un alphabetic tokens because this recipe dataset was unique.…”

Section: Startmentioning

confidence: 99%

Neural Machine Translation of Spanish-English Food Recipes Using LSTM

Dedes

Utama

Wibawa

et al. 2022

JOIV : Int. J. Inform. Visualization

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Nowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes. Based on that, this study developed a translation formula using a neural machine translation (NMT). NMT is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders. Our experiment led to novel insights and practical advice for building and extending NMT with the applied long short-term memory (LSTM) method to 47 bilingual food recipes between Spanish-English and English-Spanish. LSTM is one of the best machine learning methods for translating languages because it can retain memories for an extended period concurrently, grasp complicated connections between data, and provides highly useful information in deciding translation outcomes. The evaluation for this neural machine translation is to use BLEU. The comparing results show that the translation of recipes from Spanish-English has a better BLEU value of 0.998426 than English-Spanish with a data-sharing of 70%:30% during epoch 1000. Researchers can convert the country's popular cuisine recipes into another language for further research, allowing it to become more widely recognized abroad.

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“…The literature reviews about applied text preprocessing on text classification and question classification show that text preprocessing is a key for success classification accuracy. The authors in [10] studied the impact of data preprocessing for prediction review ratings. They compared the different methods for preprocessing.…”

Section: Text Preprocessingmentioning

confidence: 99%

Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

Chotirat¹,

Meesad²

2021

Heliyon

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Question classification is a crucial task for answer selection. Question classification could help define the structure of question sentences generated by features extraction from a sentence, such as who, when, where, and how. In this paper, we proposed a methodology to improve question classification from texts by using feature selection and word embedding techniques. We conducted several experiments to evaluate the performance of the proposed methodology using two different datasets (TREC-6 dataset and Thai sentence dataset) with term frequency and combined term frequency-inverse document frequency including Unigram, Unigram+Bigram, and Unigram + Trigram as features. Machine learning models based on traditional and deep learning classifiers were used. The traditional classification models were Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine. The deep learning techniques were Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Hybrid model, which combined CNN and BiLSTM model. The experiment results showed that our methodology based on Part-of-Speech (POS) tagging was the best to improve question classification accuracy. The classifying question categories achieved with average micro 𝐹 1 -score of 0.98 when applied SVM model on adding all POS tags in the TREC-6 dataset. The highest average micro 𝐹 1 -score achieved 0.8 when applied GloVe by using CNN model on adding focusing tags in the Thai sentences dataset.

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The impact of text preprocessing on the prediction of review ratings

Cited by 28 publications

References 11 publications

Improving automated Turkish text classification with learning‐based algorithms

Improving automated Turkish text classification with learning‐based algorithms

Neural Machine Translation of Spanish-English Food Recipes Using LSTM

Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

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