An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Kogilavani, S. V.; Sampath, Sai Haritha; Nandhakumar, Pramod; Mahalingam, Prasath; Malliga, S.; Kumaresan, Prasanna Kumar; Priyadharshini, Ruba

doi:10.1016/j.csl.2022.101407

Cited by 16 publications

(3 citation statements)

References 11 publications

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“…6 ( Sharma et al, 2021 ). The RNN architecture is also modified to model the history and future generated context ( Shanmugavadivel et al, 2022 ). The neurons are split into the parts responsible for the forward and backward directions.…”

Section: Methodsmentioning

confidence: 99%

The neural machine translation models for the low-resource Kazakh–English language pair

Karyukin

Rakhimova

Karibayeva

et al. 2023

PeerJ Computer Science

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The development of the machine translation field was driven by people’s need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages by artificially increasing the size of the corpora and improving the performance of the Kazakh–English machine translation models. These methods are called forward translation, backward translation, and transfer learning. Then the Sequence-to-Sequence (recurrent neural network and bidirectional recurrent neural network) and Transformer neural machine translation architectures with their features and specifications are concerned for conducting experiments in training models on parallel corpora. The experimental part focuses on building translation models for the high-quality translation of formal social, political, and scientific texts with the synthetic parallel sentences from existing monolingual data in the Kazakh language using the forward translation approach and combining them with the parallel corpora parsed from the official government websites. The total corpora of 380,000 parallel Kazakh–English sentences are trained on the recurrent neural network, bidirectional recurrent neural network, and Transformer models of the OpenNMT framework. The quality of the trained model is evaluated with the BLEU, WER, and TER metrics. Moreover, the sample translations were also analyzed. The RNN and BRNN models showed a more precise translation than the Transformer model. The Byte-Pair Encoding tokenization technique showed better metrics scores and translation than the word tokenization technique. The Bidirectional recurrent neural network with the Byte-Pair Encoding technique showed the best performance with 0.49 BLEU, 0.51 WER, and 0.45 TER.

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Section: Methodsmentioning

confidence: 99%

The neural machine translation models for the low-resource Kazakh–English language pair

Karyukin

Rakhimova

Karibayeva

et al. 2023

PeerJ Computer Science

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“…Before encoding, text pre-processing is carried out to clean up the data [20]. Preparing data for use by eliminating and discarding extraneous text that does not add value to the model and instead decreases its quality is a technique known as text pre-processing [21], [22]. Natural language toolkit (NLTK) and 're' are the two Python packages used for text pre-processing.…”

Section: Data Pre-processingmentioning

confidence: 99%

Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals

Abu Samah,

Nor Azharludin,

Riza

et al. 2023

IJ-AI

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Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.

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“…Empirical study was performed on prompt-based sentiment analysis and emotion detection 19 in order to understand the bias towards pre-trained models applied for affective computing. The findings suggest that the number of label classes, emotional label-word selections, prompt templates and positions, and the word forms of emotion lexicons are factors that biased the pre-trained models 20 .…”

Section: Related Workmentioning

confidence: 99%

Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data

Shanmugavadivel¹,

Sathishkumar

Raja³

et al. 2022

Sci Rep

Self Cite

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Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data. Code-mixed data is framed by combining words and phrases from two or more distinct languages in a single text. It is quite challenging to identify emotion or offensive terms in the comments since noise exists in code-mixed data. The majority of advancements in hostile language detection and sentiment analysis are made on monolingual data for languages with high resource requirements. The proposed system attempts to perform both sentiment analysis and offensive language identification for low resource code-mixed data in Tamil and English using machine learning, deep learning and pre-trained models like BERT, RoBERTa and adapter-BERT. The dataset utilized for this research work is taken from a shared task on Multi task learning DravidianLangTech@ACL2022. Another challenge addressed by this work is the extraction of semantically meaningful information from code-mixed data using word embedding. The result represents an adapter-BERT model gives a better accuracy of 65% for sentiment analysis and 79% for offensive language identification when compared with other trained models.

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An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Cited by 16 publications

References 11 publications

The neural machine translation models for the low-resource Kazakh–English language pair

The neural machine translation models for the low-resource Kazakh–English language pair

Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals

Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data

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