Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques

Adhikari, Surabhi; Thapa, Surendrabikram; Naseem, Usman; Singh, Priyanka; Huo, Huan; Bharathy, Gnana; Prasad, Mukesh

doi:10.1016/j.ijhcs.2021.102761

Cited by 25 publications

(7 citation statements)

References 35 publications

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“…Next, digits, punctuation marks, and extra spaces that do not provide any semantic information to the text were removed. NLP classification tasks often involve removing stop words to improve performance metrics [39]. However, in this dataset, worry feelings were frequently expressed as ideas about oneself, leading to the use of the "I" and "my" pronouns.…”

Section: ) Data Preprocessingmentioning

confidence: 96%

Building and Testing Fine-Grained Dataset of COVID-19 Tweets for Worry Prediction

Alharbi¹,

Fkih²

2022

IJACSA

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The COVID-19 outbreak has resulted in the loss of human life worldwide and has increased worry concerning life, public health, the economy, and the future. With lockdown and social distancing measures in place, people turned to social media such as Twitter to share their feelings and concerns about the pandemic. Several studies have focused on analyzing Twitter users' sentiments and emotions. However, little work has focused on worry detection at a fine-grained level due to the lack of adequate datasets. Worry emotion is associated with notions such as anxiety, fear, and nervousness. In this study, we built a dataset for worry emotion classification called "WorryCov". It is a relatively large dataset derived from Twitter concerning worry about COVID-19. The data were annotated into three levels ("noworry", "worry", and "high-worry"). Using the annotated dataset, we investigated the performance of different machine learning algorithms (ML), including multinomial Naïve Bayes (MNB), support vector machine (SVM), logistic regression (LR), and random forests (RF). The results show that LR was the optimal approach, with an accuracy of 75%. Furthermore, the results indicate that the proposed model could be used by psychologists and researchers to predict Twitter users' worry levels during COVID-19 or similar crises.

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Section: ) Data Preprocessingmentioning

confidence: 96%

Building and Testing Fine-Grained Dataset of COVID-19 Tweets for Worry Prediction

Alharbi¹,

Fkih²

2022

IJACSA

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“…Apart from this, there have also been some works in medical NLP for the Nepali language. Adhikari et al [16] used the data from DemetiaBank [43] and performed manual translations to create a Nepali Alzheimer's disease dataset of transcripts from 168 Alzheimer's disease (AD) patients and 98 Control normal (CN) participants. The dataset consisted of a total of 499 transcripts with 255 transcripts belonging to AD patients and 244 belonging to CN participants.…”

Section: The Pressing Need Of Data In Nepali Lan-guagementioning

confidence: 99%

“…Over time, the field of NLP in Nepali has witnessed certain advancements. However, given the morphologically rich nature of the language [12] and the complex sentence structure [16], NLP in Nepali is particularly challenging and demands vigorous research. NLP advancements in any low-resource language like Nepali are usually restricted by the lack of pretraining data, resource uniformity, and computing resources [35].…”

Section: The Pressing Need Of Data In Nepali Lan-guagementioning

confidence: 99%

“…The complex and monographically rich nature of the Nepali language makes Natural Language Processing (NLP) tasks particularly challenging for this language [12]. Unlike languages such as English, which has ample resources and a plethora of NLP studies [13], [14], research in the field of low-resource languages such as Nepali is scarce [15], [16]. There have been some works involving sentimental analysis in Nepali [12], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

Rauniyar,

Poudel,

Shiwakoti

et al. 2023

IEEE Access

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In today's social media-dominated landscape, digital platforms wield substantial influence over public opinion, particularly during crucial political events such as electoral processes. These platforms become hubs for diverse discussions, encompassing topics, reforms, and desired changes. Notably, in times of government dissatisfaction, they serve as arenas for anti-establishment discourse, highlighting the need to analyze public sentiment in these conversations. However, the analysis of such discourse is notably scarce, even in high-resource languages, and entirely non-existent in the context of the Nepali language. To address this critical gap, we present Nepal Anti Establishment discourse Tweets (NAET), a novel dataset comprising 4,445 multi-aspect annotated Nepali tweets, facilitating a comprehensive understanding of political conversations. Our contributions encompass evaluating tweet relevance, sentiment, and satire, while also exploring the presence of hate speech, identifying its targets, and distinguishing directed and non-directed expressions. Additionally, we investigate hope speech, an underexplored aspect crucial in the context of antiestablishment discourse, as it reflects the aspirations and expectations from new political figures and parties. Furthermore, we set NLP-based baselines for all these tasks. To ensure a holistic analysis, we also employ topic modeling, a powerful technique that helps us identify and understand the prevalent themes and patterns emerging from the discourse. Our research thus presents a comprehensive and multi-faceted perspective on anti-establishment election discourse in a low-resource language setting. The dataset is publicly available, facilitating in-depth analysis of political tweets in Nepali discourse and further advancing NLP research for the Nepali language through labeled data and baselines for various NLP tasks. The dataset for this work is made available at https://github.com/rkritesh210/NAET.

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“…Being a low-resource language, only a few studies have been performed on the detection of hate speech in Nepali. Similarly, in general itself, for other tasks, we also have fewer resources available due to the limited research and data availability for the language [1,33]. Shrestha et al [29] In the analysis of performing benchmark classification, Multilingual BERT (M-BERT) which is trained using Wikipedia dump for multiple languages, did not perform well compared to traditional ML models.…”

Section: Work In Nepali Languagementioning

confidence: 99%

NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse

Thapa,

Rauniyar,

Shiwakoti

et al. 2023

Frontiers in Artificial Intelligence and Applications

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The use of social media during election campaigns has become increasingly popular. However, the unbridled nature of online discourse can lead to the propagation of hate speech, which has far-reaching implications for the democratic process. Natural Language Processing (NLP) techniques are being used to counteract the spread of hate speech and promote healthy online discourse. Despite the increasing need for NLP techniques to combat hate speech, research on low-resource languages such as Nepali is limited, posing a challenge to the realization of the United Nations’ Leave No One Behind principle, which calls for inclusive development that benefits all individuals and communities, regardless of their backgrounds or circumstances. To bridge this gap, we introduce NEHATE, a large-scale manually annotated dataset of hate speech and its targets in Nepali local election discourse. The dataset comprises 13,505 tweets, annotated for hate speech with further sub-categorization of hate speech into targets such as community, individual, and organization. Benchmarking of the dataset with various algorithms has shown potential for performance improvement. We have made the dataset publicly available at https://github.com/shucoll/NEHate to promote further research and development, while also contributing to the UN SDGs aimed at fostering peaceful, inclusive societies, and justice and strong institutions.

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Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques

Cited by 25 publications

References 35 publications

Building and Testing Fine-Grained Dataset of COVID-19 Tweets for Worry Prediction

Building and Testing Fine-Grained Dataset of COVID-19 Tweets for Worry Prediction

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse

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