Detecting Sentiment in Nepali texts: A bootstrap approach for Sentiment Analysis of texts in the Nepali language

Gupta, Chandan Prasad; Bal, Bal Krishna

doi:10.1109/ccip.2015.7100739

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Gupta et al [27] provided two key approaches for Nepali text sentiment analysis. In the first approach, they established a method wherein terms with emotional connotations are found in texts written in Nepali to determine the mood of the document.…”

Section: B Natural Language Processing In Nepali Languagementioning

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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Section: B Natural Language Processing In Nepali Languagementioning

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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“…However, there is no such work explored on the aspectbased sentiment analysis in the Nepali language. [18] developed a Nepali Sentiment Corpus which is a collection of sentences from the News domain with binary-level annotation whether it is subjective or objective. They also developed Nepali SentiWordNet called Bhavanakos, which is a translated version of English SentiWordNet.…”

Section: Related Workmentioning

confidence: 99%

“…It is one of the important tool responsible for directly or indirectly impacting lives of people in the online community and can impact the growth/sales of various products and businesses. Early researchers [1], [2] classify the sentiment in a sentence level, be it a comment or a text-piece. However, there is a growing trend of research in Aspect Based Sentiment Analysis (ABSA) for product reviews [3]- [5] to identify the positive and negative sentiment of various aspects in the given text related to the restaurant and electronics product domain.…”

Section: Introductionmentioning

confidence: 99%

Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

Singh

Timilsina

Bal

et al. 2020

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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With the increase in internet access and the ease of writing comments in the Nepali language, fine-grained sentiment analysis of social media comments is becoming more and more pertinent. There are a number of benchmarked datasets for high-resource languages (English, French, and German) in specific domains like restaurants, hotels or electronic goods but not in low-resource languages like Nepali. In this paper, we present our work to create a dataset for the targeted aspect-based sentiment analysis in the social media domain, set up a dataset benchmark and evaluate using various machine learning models. The dataset comprises of code-mixed and code-switched comments extracted from Nepali YouTube videos. We present convincing baselines using a multilingual BERT model for the Aspect Term Extraction task and BiLSTM model for the Sentiment Classification Task achieving 57.978% and 81.60% F1 score respectively.

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“…Researches have been performed on detecting sentiments (Gupta & Bal, 2015) and classifying sentiments (Thapa & Bal, 2016)…”

Section: Introductionmentioning

confidence: 99%

Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

Tamrakar

Bal

Thapa

2020

Tech J

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Aspect-based Sentiment Analysis assists in understanding the opinion of the associated entities helping for a better quality of a service or a product. A model is developed to detect the aspect-based sentiment in Nepali text using Machine Learning (ML) classifier algorithms namely Support Vector Machine (SVM) and Naïve Bayes (NB). The system collects Nepali text data from various websites and Part of Speech (POS) tagging is applied to extract the desired features of aspect and sentiment. Manual labeling is done for each sentence to identify the sentiment of the sentence. Term Frequency – Inverse Document Frequency (TF-IDF) is applied to compute the importance of the words. The feature vectors thus produced are then applied to the Classifier algorithms to predict and classify the sentence. The accuracy obtained by the SVM classifier is 76.8% whereas Bernoulli NB is 77.5%.

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Detecting Sentiment in Nepali texts: A bootstrap approach for Sentiment Analysis of texts in the Nepali language

Cited by 9 publications

References 4 publications

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

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

Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

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