Abstract
Lockdowns, working from home, staying at home, and physical distance are expected to significantly impact consumer attitudes and behaviors during the COVID-19 pandemic. During the implementation of the Movement Control Order, Malaysians’ food preferences are already shifting away, influencing new consumption behavior. Since it has played a significant role in many areas of natural language, mainly using social media data from Twitter, there has been increased interest in sentiment analysis in recent years. However, research on the performance of various sentiment analysis methodologies such as n-gram ranges, lexicon techniques, deep learning, word embedding, and hybrid methods within this domain-specific sentiment is limited. This study evaluates several approaches to determine the best approach for tweets on food consumption behavior in Malaysia during the COVID-19 pandemic. This study combined unigram and bigram ranges with two lexicon-based techniques, TextBlob and VADER, and three deep learning classi-fiers, Long Short-Term Memory Network (LSTM), Convolutional Neural Networks (CNN), and their hybridization. Word2Vector and GloVe are two-word embedding approaches used by LSTM-CNN. The embedding GloVe on TextBlob approach with a combination of Unigram + Bigram [1,2] range produced the best results, with 85.79% accuracy and 85.30% F1-score. According to these findings, LSTM outperforms other classifiers because it achieves the highest scores for both performance metrics. The classification performance can be improved in future studies if the dataset is more evenly distributed across each positive and negative label.
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Acknowledgements
The authors would like to thank Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) and Universiti Teknologi MARA. The registration fees is funded by Pembiayaan Yuran Procding Berindeks (PYPB), Tabung Dana Kecemerlangan Pendidikan (DKP), Universiti Teknologi MARA (UiTM), Malaysia.
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Ramzi, N.I., Yusoff, M., Noh, N.M. (2023). Comparison Analysis of LSTM and CNN Variants with Embedding Word Methods for Sentiment Analysis on Food Consumption Behavior. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_14
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