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The Fourth Industrial Revolution (4.0) is a fusion of advances in Artificial Intelligence (AI), Robotics, the Internet of Things (IoT), Genetic Engineering, Quantum Computing, and other technologies. A large number of people are using internet-based services as a result of enhanced internet infrastructure and decreased costs. As a result, such businesses' attempts to penetrate internet media are disrupted. The e-commerce company, like Amazon, offers both customer-to-customer and business-to-business services in the apparel sector. Companies must understand the needs of buyers to maximize their profits. As a result, consumer sentiment analysis is carried out. However, because this procedure is time-consuming, it is made automatically utilizing artificial intelligence approaches. According to the findings of a study on sentiment analysis on an E-Commerce-based web store for women, the apparels review dataset using the CNN method with the word vector generator and TF-IDF can produce a higher accuracy of 94%.

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Tweet sentiment analysis is a Deep Learning study that is beneficial for automatically determining public opinion on a certain topic. Using the Long Short-Term Memory (LSTM) algorithm, this paper aims to proposes a Twitter analysis technique that divides Tweets into two categories (positive and negative). The Global Vector (GloVe) word embedding score is used to rate many selected words as network input. GloVe converts words into vectors by building a corpus matrix. The GloVe outperforms its prior model, owing to its smaller vector and corpora sizes. GloVe has a higher accuracy than the model word embedding word2vec, Continuous Bag of Word(CBoW), and word2vec Skip-gram. The preprocessed term variation was conducted to test the performance of sentiment classification. The test results show that this proposed method has succeeded in classifying with the best results with an accuracy of 95.61%.