Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech a...Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.展开更多
The tea packing design occupies the important position in the field of the graphic design. A successful tea packing design can affect the consumers＇ desire to buy. In this article, from the tea packaging colors, form...The tea packing design occupies the important position in the field of the graphic design. A successful tea packing design can affect the consumers＇ desire to buy. In this article, from the tea packaging colors, forms, shapes and so on, the author discusses the vision, touch, taste and other sensory stimulation, and analyzes the relationship between the constituent elements including the emotional factors and the psychological needs of the consumers, as well as the packaging design.展开更多
文摘Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.
文摘The tea packing design occupies the important position in the field of the graphic design. A successful tea packing design can affect the consumers＇ desire to buy. In this article, from the tea packaging colors, forms, shapes and so on, the author discusses the vision, touch, taste and other sensory stimulation, and analyzes the relationship between the constituent elements including the emotional factors and the psychological needs of the consumers, as well as the packaging design.
