BEmoC: A Corpus for Identifying Emotion in Bengali Texts

Iqbal, Mohammad; Das, Avishek; Sharif, Omar; Hoque, Mohammed Moshiul; Sarker, Iqbal H.

doi:10.1007/s42979-022-01028-w

Cited by 12 publications

(13 citation statements)

References 46 publications

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“…Different emotions' corpus on Bengali text was created to enrich the Bengali NLP. ‘BEmoC’, a corpus containing 7000 Bengali texts gathered from Facebook posts or comments, YouTube comments and different online blog posts were constructed including six basic emotion classes [15] . Emotion detection from Facebook comments using deep learning techniques was studied in [16] .…”

Section: Literature Reviewmentioning

confidence: 99%

A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text

Lora,

Jahan,

Hussain

et al. 2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text

Lora,

Jahan,

Hussain

et al. 2023

Heliyon

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“…Using a transformer-based technique (Bangla-BERT), they achieved top scores of 92% in identifying aggressive texts. A recent study (Sharif and Hoque, 2022) introduced a Bangla aggressive text dataset (BAD). Using a weighted ensemble of m-BERT, distil-BERT, Bangla-BERT, and XLM-R, they achieved top scores of 93.43% (coarse-grained) and 93.11% (fine-grained) in identifying and categorizing aggressive Bangla texts.…”

Section: Related Workmentioning

confidence: 99%

“…They can quickly spread information to millions of people. Thus, identifying and categorizing aggressive texts on social media is paramount in maintaining online safety, fostering positive digital interactions, and preventing dissemination of harmful or offensive content (Sharif and Hoque, 2022). Real-world problems like relational anger or even violence are significant problems since threats and insults made online can occasionally result in actual hurt.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

2023

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Although Spanish is one of the most spoken languages in the world, it was only until very recently that the development of linguistic technologies for it had a strong boost. However, this is not entirely true for some of its Latin American variants, such as the Mexican Spanish, which show phonetic, and also some lexical and semantic differences with respect to peninsular Spanish. This talk will focus on presenting the development of NLP for Mexican Spanish, emphasizing the path taken through the organization of different evaluation campaigns. It will present some data about Mexican Spanish as well as about the impact of the organization of shared tasks in the context of IberLEF for the development of the NLP area in our country, first as a mechanism to motivate more students to get involved, and then as a vehicle to build resources and design and implement specific methods. The talk will conclude by exposing some of the obstacles faced, our main achievements, and some plans for the coming years.Bio: Manuel Montes is Full Professor at the National Institute of Astrophysics, Optics and Electronics (INAOE) of Mexico. His research is on automatic text processing. He is author of more than 250 journal and conference papers in the fields of information retrieval, text mining and authorship analysis. He has been visiting professor at the Polytechnic University of Valencia (Spain), and the University of Alabama (USA). He is also member of the Mexican Academy of Sciences (AMC), and founding member of the Mexican Academy of Computer Science (AMEXCOMP), and the Mexican Association of Natural Language Processing (AMNLP). In the context of the latter, he has been the organizer of

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“…With an accuracy of 83 percent when considering n-gram as a feature, LR outperformed SVM and NB. Iqbal et al [9] proposed a four-step process for categorizing six emotions in Bengali literature, including data crawling, preprocessing, labelling, and verification, with 7,000 texts labeled into six basic emotion groups. The dataset is graded with a score of 0.969.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis on Bangla conversation using machine learning approach

Hassan

Shakil²,

Moon³

et al. 2022

IJECE

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<span>Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.</span>

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BEmoC: A Corpus for Identifying Emotion in Bengali Texts

Cited by 12 publications

References 46 publications

A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text

A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

Sentiment analysis on Bangla conversation using machine learning approach

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