Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
The sentiment analysis of women's sportswear brands on e-commerce platforms using long short-term memory (LSTM) networks is explored in this study. Evaluating sentiment towards brands is crucial for understanding consumer preferences and market trends. The study focuses on sentiment analysis as...
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| Format: | Thesis |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/96276/1/96276.pdf |
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| Summary: | The sentiment analysis of women's sportswear brands on e-commerce platforms using long short-term memory (LSTM) networks is explored in this study. Evaluating sentiment towards brands is crucial for understanding consumer preferences and market trends. The study focuses on sentiment analysis as it pertains to women's sportswear brands, aiming to provide insights into customer satisfaction and perception. Effective sentiment analysis enables businesses to make informed decisions regarding product development, marketing strategies, and brand positioning.
Leveraging LSTM networks, known for their ability to capture sequential patterns in data, the study achieves a comprehensive understanding of customer sentiment towards women's sportswear brands. Through meticulous data pre-processing and analysis techniques, the study offers valuable insights into consumer behaviour and preferences in the e-commerce domain. Utilizing the powerful LSTM model known for its proficiency in learning model layer representations from data processing, the system achieves an impressive accuracy of 90% and above |
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