RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

Pradhan, Rahul; Sharma, Dilip Kumar

doi:10.1007/s00500-022-07091-y

Cited by 15 publications

(3 citation statements)

References 50 publications

(27 reference statements)

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“…Lately, a research study was conducted that employed ensemble learning with a language model. [13] developed an ensemble model that extracted sentence features using two specific transformers, namely XLM-RoBERTa and USE, to code-mix (i.e., Hindi and English language) Tweet dataset. The model outperformed the contemporary baseline models by 2% in terms of ACC.…”

Section: B Ensemble Technique In Sentimental Analysismentioning

confidence: 99%

“…Modern data-driven techniques, such DL along with state-ofthe-art transformers, have demonstrated significant potential for sentiment classification [8], [9]. Recently, with ensemble learning techniques, a collection of learning models are generated either in parallel or sequentially, and wherein separate predictions are merged [10], have been employed in sentiment classification which demonstrates strong generalizability gains [11]- [13]. Nonetheless, the number of studies that have employed ensemble learning in sentiment classification is still limited, especially when it comes to examination of the robustness of the model.…”

Section: Introductionmentioning

confidence: 99%

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SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation

Thiengburanathum,

Charoenkwan

2023

IEEE Access

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Sentiment classification of social media posts is among the most challenging and timeconsuming tasks for analysts. This is particularly true when applied to languages that employ scriptio continua, such as the Thai language, in which there are no spaces between written words and where there is no end of sentence punctuation. Thai is considered a scarce-resource language as few datasets are available to researchers. Although machine-learning (ML) and deep-learning (DL) algorithms can identify sentiment classification polarity, the performance of the existing classification models are still inadequate. This study proposes a novel stacking ensemble learning technique for identifying sentiment classification polarity in the Thai language, SETAR. Our stacking ensemble strategy utilized the pre-trained Thai language model (WangChanBERTa), based on a Robustly Optimized BERT Pretraining Approach (RoBERTa) architecture to form a feature vector. This feature was combined with three distinct feature vectors obtained from three well-known categories, namely Word2Vec, TF-IDF, and bag-of-words, as a new hybrid sentence representation. The base learners were trained using seven chosen complex heterogeneous ML algorithms, including support vector machine (SVM), random forest (RF), extremely randomized trees (ET), light gradient boosting machine (LGBM), multi-layer perceptron (MLP), partial least squares (PLS), and logistic regression (LR) to enable the development of the final meta-learners. The results revealed that our proposed stacking ensemble model outperformed the baseline models of all classification metrics among the training and test sets, as was determined by extensive benchmarking, carried out on the four datasets, which included our developed sentiment corpus that domain experts annotated.

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Section: B Ensemble Technique In Sentimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation

Thiengburanathum,

Charoenkwan

2023

IEEE Access

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“…To enhance the features of the dataset, various preprocessing methodologies can be applied 28 and in case of multifocus images, the neural networks aid in image fusion 29 . Also hybrid models based on fuzzy logic, 30 blockchain, 31 random forest, 32 DL, 33 – 35 and extreme DL and ML 36 – 39 can be incorporated.…”

Section: Introductionmentioning

confidence: 99%

Cropland prediction using remote sensing, ancillary data, and machine learning

Katal

Hooda

Sharma

et al. 2022

J. Appl. Rem. Sens.

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Temporal and spatial environmental factors have a substantial influence on crop yields, and an accurate prediction can benefit timely decision-making in global food production. Thus for better agricultural management, the precise estimation of the croplands is helpful. Mapping the cropland dynamics with regular requirement of crops is an important prerequisite for monitoring crops, yield estimation, and crop inventories. Remote sensing and geographic information systems play a significant role in tracing and understanding environmental impacts of agriculture. The use of machine learning aids in developing a model that can give precise predictions based on the historical data. The main objective of our study is to use these machine learning algorithms to make accurate predictions about the best crop types using the spectral, temporal, and polarimetric features. A big dataset incorporating optical and polarimetric aperture radar experimental values is used to train the machine learning classifiers for predicting the right crop type in the study area. It has been observed that these features aid in accurate mapping of the cropland. Our study involves the performance comparison of the various machine learning algorithms, and it has been observed that a single-layer neural network offers prediction accuracy of ∼99.6% for this big PoISAR dataset.

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A Survey on Code-Mixed Sentiment Analysis Based on Hinglish Dataset

Baghel

2023

Lecture Notes in Networks and Systems

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RETRACTED ARTICLE: An ensemble deep learning classifier for sentiment analysis on code-mix Hindi–English data

Cited by 15 publications

References 50 publications

SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation

SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation

Cropland prediction using remote sensing, ancillary data, and machine learning

A Survey on Code-Mixed Sentiment Analysis Based on Hinglish Dataset

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