HumourHindiNet: Humour detection in Hindi web series using word embedding and convolutional neural network

Tools

Kumar, Akshi; Mallik, Abhishek and Kumar, Sanjay. 2024. HumourHindiNet: Humour detection in Hindi web series using word embedding and convolutional neural network. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(7), 98. ISSN 2375-4699 [Article]

Preview

Text
ACM_Tallip_Hindi_Humor_R2-6.pdf - Accepted Version
Download (944kB) | Preview

Official URL: https://dl.acm.org/doi/10.1145/3661306

Abstract or Description

Humour is a crucial aspect of human speech, and it is, therefore, imperative to create a system that can offer such detection. While data regarding humour in English speech is plentiful, the same cannot be said for a low-resource language like Hindi. Through this paper, we introduce two multimodal datasets for humour detection in the Hindi web series. The dataset was collected from over 500 minutes of conversations amongst the characters of the Hindi web series Kota−Factory and Panchayat. Each dialogue is manually annotated as Humour or Non-Humour. Along with presenting a new Hindi language-based Humour detection dataset, we propose an improved framework for detecting humour in Hindi conversations. We start by preprocessing both datasets to obtain uniformity across the dialogues and datasets. The processed dialogues are then passed through the Skip-gram model for generating Hindi word embedding. The generated Hindi word embedding is then passed onto three convolutional neural network (CNN) architectures simultaneously, each having a different filter size for feature extraction. The extracted features are then passed through stacked Long Short-Term Memory (LSTM) layers for further processing and finally classifying the dialogues as Humour or Non-Humour. We conduct intensive experiments on both proposed Hindi datasets and evaluate several standard performance metrics. The performance of our proposed framework was also compared with several baselines and contemporary algorithms for Humour detection. The results demonstrate the effectiveness of our dataset to be used as a standard dataset for Humour detection in the Hindi web series. The proposed model yields an accuracy of 91.79 and 87.32 while an F1 score of 91.64 and 87.04 in percentage for the Kota−Factory and Panchayat datasets, respectively.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1145/3661306

Additional Information:

"© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Asian and Low-Resource Language Information Processing, http://dx.doi.org/10.1145/3661306."

Keywords:

Convolutional Neural Network (CNN), Hindi Web Series, Humour Detection, Long Short-Term Memory (LSTM), Low-Resource Languages, Social networks, Skip-gram Hindi Word Embedding

Departments, Centres and Research Units:

Computing

Dates: