Beauty products are an integral part of daily routines for many women. The beauty industry continually evolves, introducing innovative formulations and trends that cater to diverse preferences and needs. Sentiment analysis of beauty products is crucial as it allows companies to understand customer opinions, preferences, and experiences. In this study, we explore sentiment analysis in the context of beauty products. We collected data from various platforms like shajgoj.com, kireibd.com, and klassy.com.bd, resulting in a dataset of 4431 reviews. Employing this unique dataset, we conducted a comprehensive analysis, employing a trio of neural network architectures: the foundational simple neural network, the robust 1D convolutional neural network (1D CNN), and the intricate long short-term memory (LSTM). Upon completing data preprocessing, we incorporated word embeddings derived from the GloVe 100D file, resulting in the attainment of an accuracy level of 78.78%. By combining data collection, model experimentation, and embedding techniques, we gain a multifaceted view, offering deeper insights into beauty product sentiment. In addition to advancing sentiment analysis methodologies, our study signifies a positive step toward empowering beauty industry stakeholders with actionable intelligence.