Sentiment analysis is a key technique in natural language processing that enables computers to understand human emotions expressed in text. It is widely used in applications such as customer feedback analysis, social media monitoring, and product reviews. However, sentiment analysis of customer reviews presents unique challenges, including the need for large datasets and the difficulty in accurately capturing subtle emotional nuances in text. In this paper, we present a comparative study of sentiment analysis on customer reviews using both deep learning and traditional machine learning techniques. The deep learning models include Convolutional Neural Network (CNN) and Recursive Neural Network (RNN), while the machine learning methods consist of Logistic Regression, Random Forest, and Naive Bayes. Our dataset is composed of Amazon product reviews, where we utilize the star rating as a proxy for the sentiment expressed in each review. Through comprehensive experiments, we assess the performance of each model in terms of accuracy and effectiveness in detecting sentiment. This study provides valuable insights into the strengths and limitations of both deep learning and traditional machine learning approaches for sentiment analysis.

Sentiment analysis is a key technique in natural language processing that enables computers to understand human emotions expressed in text. It is widely used in applications such as customer feedback analysis, social media monitoring, and product reviews. However, sentiment analysis of customer reviews presents unique challenges, including the need for large datasets and the difficulty of accurately capturing subtle emotional nuances in text. In this paper, we present a comparative study of sentiment analysis on customer reviews using both deep learning and traditional machine learning techniques. The deep learning models include Convolutional Neural Networks (CNN) and Recursive Neural Networks (RNN), while the machine learning methods consist of Logistic Regression, Random Forest, and Naive Bayes. Our dataset is composed of Amazon product reviews, where we utilize the star rating as a proxy for the sentiment expressed in each review. Through comprehensive experiments, we assess the performance of each model in terms of accuracy and effectiveness in detecting sentiment. This study provides valuable insights into the strengths and limitations of both deep learning and traditional machine learning approaches for sentiment analysis.

This paper studies dynamic scheduling of a self-interested battery charging station that provides fully-charged batteries for electric vehicle (EV) battery swapping services. The charging station receives multi-type battery orders from the demand side, and it can refuse the orders or admit part of the orders according to current system states. If admitted, battery orders have to be served completely before predefined deadlines. Based on Lyapunov optimization framework, a dynamic scheduling approach is developed, which allows the charging station to observe real-time system states and make scheduling decisions in an online fashion. In theoretical analysis, the feasibility and suboptimality of the proposed approach are proven. Based on the analysis, the feasible ranges of algorithm parameters are derived, ensuring that battery orders can be completed before deadlines. In simulation, actual real-time electricity data is used. The results show that the proposed approach satisfies the deadline constraints and achieves higher profit than other benchmark approaches.