Man is What He Eats: A Research on Hinglish Sentiments of YouTube Cookery Channels Using Deep Learning
Suraj Kumar Donthula1, Abhishek Kaushik2
1Suraj Kumar Donthula, Department of Computing, Dublin Business School, D02 WC04 Dublin, Ireland.
2Abhishek Kaushik, ADAPT Centre, Department of Computing, Dublin City University, D09 W6Y4 Dublin, Ireland.
Manuscript received on 12 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 02 November 2019 | PP: 930-937 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11530982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1153.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Our study focuses on the sentiment analysis of Hinglish comments by multi-label text classification on cookery channels of YouTube using Deep learning. Multi-layer perceptron (MLP) with different parameters was implemented in our study to investigate the various sentiments in the comments. We have modelled and evaluated MLP by varying the number of neurons, layers, optimizers, activation functions with the various feature engineering methods such as tf-idf, count vectorizer, pre-trained embeddings and customize dembeddings. These experiments were conducted on two datasets they are Kabita’s Kitchen and Nisha Madhulika’s dataset. From the investigation, we concludedKabita’s Kitchen dataset has the highest accuracy 98.53% and Nisha Madulika’shas 98.48% accuracy in MLP. This outcome of the experiment was evaluated based on careful analysis on tests conducted during our study.
Keywords: Cookery Channels, Multi-layer Perceptron, Hinglish, Sentiment Analysis.
Scope of the Article: Deep Learning