Deep learning based bi-polar sentiment classification of movie reviews in Hindi

Sharma, Ankita; Ghose, Udayan

doi:10.47974/JSMS-1030

Open Access Research Article

Deep learning based bi-polar sentiment classification of movie reviews in Hindi

^*Ankita SharmaCorresponding authorankitasharma2711@gmail.comUniversity School of Information, Communication and Technology Guru Gobind Singh Indraprastha UniversitySector – 16C, Dwarka, New Delhi, 110078, IndiaView full profile → , Udayan Ghoseudayan@ipu.ac.inUniversity School of Information, Communication and Technology Guru Gobind Singh Indraprastha UniversityUniversity School of Information, Communication and Technology Guru Gobind Singh, Indraprastha University Delhi IndiaSector – 16C, Dwarka, New Delhi, 110078, IndiaView full profile →

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pp. 59–86Vol. 27Issue 1January 2024DOI: 10.47974/JSMS-1030XML

Received:: 09 Aug 2022
Accepted:: 13 Dec 2022
Published Online:: 08 Jan 2024
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1030
Pages:: 59–86

Abstract

With evolving technology, Hindi web content is growing considerably and catching popularity as the larger audience feels more connected and heard using their native language. A volcanic growth in online movie reviews (MRs) in Hindi has been observed lately; manually analyzing them is impossible. Hence, the research problem of automatic organization and classification of Hindi reviews is apparent as this can help viewers decide whether a movie is worth watching or not. This work focuses on developing a deep learning-based system for bi-polar sentiment classification of MRs for resource deficient language – Hindi. To this end, a primary Hindi movie review (MR) corpus is made and manually annotated with binary polarity class labels - positive or negative. The corpus is preprocessed using the preprocessing steps, and Random Word Embeddings (WEs) are utilized for feature extraction. This paper proposes an ensemble CNN_BiGRU, which is an integration of 1D CNN with BiGRU for the bipolar classification of Hindi MRs. To prove our ensemble’s efficacy; other widely used mainstream deep learning models (DLMs) such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) based models – Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), are also applied and compared with proposed ensemble using average classification accuracy. Empirical results show the effectiveness of the proposed ensemble in achieving a reasonably good average accuracy of 89.366%. The results indicate that proposed CNN_BiGRU compares favorably to the state-of-the-art DLMs applied and hence gives an effective solution for sentence-level bi-polar classification of MRs in a resource deficient scenario.

Keywords

Deep learning Ensemble Hindi Sentiment classification Word embeddings

Subject Classifications

Primary 68T50Secondary 97R40

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