Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

Singh, Oyesh Mann; Timilsina, Sandesh; Bal, Bal Krishna; Joshi, Anupam

doi:10.1109/asonam49781.2020.9381292

Cited by 10 publications

(10 citation statements)

References 18 publications

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“…Figure 5 shows the word-embedding process for lowresource sentiment analysis. O. M. Singh et al, [62] present a dataset benchmark to identify polarity on social media datasets and find offensive sentiment phrases in the Nepali language. Moreover, the experiment was done using genism [63] and trained 300-dimension skip-gram fast-text word embeddings on both mono and multilingual datasets.…”

Section: B Word-embeddingmentioning

confidence: 99%

“…This omission is often attributed to the utilization of online open-source data repositories, where the availability of already generated and preprocessed data obviates the need for explicit preprocessing steps. Studies like [51], [67], [62], [74], highlight the critical role of stemming and lemmatization, particularly in low-resource sentiment analysis scenarios. The absence of adequate tools for stemming and lemmatization in certain languages within NLTK necessitates the development of tailored algorithms for these specific languages.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Nonetheless, challenges persist, particularly in accurately representing all languages within tokenizer frameworks due to variations in language characters and other linguistic factors. Data cleaning [95], [35], [96], [49], [62], [65], [58], [68], [67], [89], [74], [75], [76], [77], [88], [94], [100], [103], [104], [107], [80], [106], [81], [108], [109], [82], [53], [110], [111], [83], [92], [113], [114], [115], [116], [117] Stemming [51], [67], [104], [108],…”

Section: Transfer Learningmentioning

confidence: 99%

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Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

Aliyu,

Sarlan,

Usman Danyaro

et al. 2024

IEEE Access

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The field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being nonlabour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of lowresource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers.

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Section: B Word-embeddingmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

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Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

Aliyu,

Sarlan,

Usman Danyaro

et al. 2024

IEEE Access

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“…Sentimental analysis is a major task in NLP involving any language. Singh et al [18] introduced a new Nepali sentiment analysis dataset (NepSA) by collecting comments from popular Nepali YouTube channels and videos. They extracted 3,068 comments from 37 different YouTube videos of 9 different YouTube channels and used the binary sentiment polarity schema to divide the comments into 6 aspect categories -General, Profanity, Violence, Feedback, Sarcasm, and Out-of-scope.…”

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

confidence: 99%

“…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]. These works have focused on a single aspect of sentiment analysis such as hate speech detection or abuse analysis.…”

Section: Introductionmentioning

confidence: 99%

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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