Intellectual System for Sentiment Analysis of User Reviews in E-Commerse Services

Abstract

Creating an intelligent system for analysis of user attitudes in electronic commerce services holds significant importance in today's digital landscape. An intelligent system for analyzing user attitudes allows businesses to gain deeper insights into their customers' preferences, needs, and sentiments. By examining user attitudes and behaviors, businesses can better understand customer expectations, improve their products or services, and tailor their marketing strategies accordingly. With an intelligent system in place, businesses can deliver personalized experiences to their customers. By analyzing user attitudes, preferences, and past interactions, the system can provide targeted recommendations, personalized offers, and relevant content. This level of personalization enhances customer satisfaction and fosters long-term loyalty. The ability to analyze user attitudes in real-time enables businesses to make data-driven decisions promptly. By monitoring and interpreting user sentiments, businesses can identify emerging trends, address customer concerns, and make necessary adjustments to their strategies or offerings. This agile decisionmaking process can give businesses a competitive edge in the fast-paced ecommerce industry. Online reputation [1] is crucial for businesses operating in the ecommerce domain. An intelligent system for analyzing user attitudes allows businesses to monitor and manage their online reputation effectively. By identifying negative sentiment or potential reputation risks, businesses can take proactive measures to address concerns, rectify issues, and maintain a positive brand image. Analyzing user attitudes in electronic commerce services provides valuable market intelligence.The authors of an article [2] aim to develop a proactive social-sensor service that can detect early signs of mental health issues by analyzing users' tweets. The topic is timely and relevant, as social media platforms increasingly serve as sources of valuable data for various research fields. The article provides a comprehensive overview of the proposed methodology for proactive mental health monitoring. The authors explain the steps involved in collecting and preprocessing Twitter data, as well as the application of machine learning techniques for classification and sentiment analysis. They present the results of their experiments and discuss the effectiveness of their approach in identifying potential mental health concerns.One of the notable strengths of this article is the meticulous explanation of the data collection process.