Multitasking of sentiment detection and emotion recognition in code-mixed Hinglish data

Ghosh, Soumitra; Priyankar, Amit; Ekbal, Asif; Bhattacharyya, Pushpak

doi:10.1016/j.knosys.2022.110182

Cited by 19 publications

(2 citation statements)

References 14 publications

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“…However, the terms sentiment analysis and opinion mining are sometimes used synonymously because opinion mining is a tool that contextualises polarity ratings in terms of topics, facets, and objectives. Sentiment detection is the process of categorising a sentence or text into classifications that are neutral, positive, or negative (sometimes) depending on the polarity of the sentences [2]. There are 45 million native speakers of the Nepali language throughout the world, notably in Nepal, Bhutan, Myanmar, and various regions of India, including Sikkim, West Bengal (Darjeeling district), Uttaranchal, and Assam [3].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

IJNLC

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The article represents the Sentiment Analysis (SA) and Tense Classification using Skip gram model for the word to vector encoding on Nepali language. The experiment on SA for positive-negative classification is carried out in two ways. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 scores of 0.6779 is observed for positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences. This paper also proposes using a skip-gram model to identify the tenses of Nepali sentences and verbs. In the third experiment, the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP)classification and it is observed that verb chunks had very low overall accuracy of 53%. In the fourth experiment, conducted for Tense Classification using Sentences resulted in improved efficiency with overall accuracy of 89%. Past tenses were identified and classified more accurately than other tenses. Hence, sentence based tense classification is proven to be better than verb Chunker based sentiment analysis.

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Section: Introductionmentioning

confidence: 99%

Untitled

2023

IJNLC

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“…[1] However, the terms sentiment analysis and opinion mining are sometimes used synonymously because opinion mining is a tool that contextualises polarity ratings in terms of topics, facets, and objectives. Sentiment detection is the process of categorising a sentence or text into classifications that are neutral, positive, or negative (sometimes) depending on the polarity of the sentences [2]. There are 45 million native speakers of the Nepali language throughout the world, notably in Nepal, Bhutan, Myanmar, and various regions of India, including Sikkim, West Bengal (Darjeeling district), Uttaranchal, and Assam [3].…”

Section: Introductionmentioning

confidence: 99%

Chunker Based Sentiment Analysis for Nepali Text

Yajnik,

Tamang

2023

Artificial Intelligence, NLP , Data Science and Cloud Computing Technology

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The article represents the Sentiment Analysis (SA) of a Nepali sentence. Skip-gram model is used for the word to vector encoding. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 score of 0.6779 is observedfor positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences.

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