Sentiment analysis is a branch of Natural Language Processing (NLP) and machine learning in which text is classified into two categories: positive and negative. Because the use of the internet and social media is growing at a rapid pace, the products created by these two are receiving far more client input than in the past. Text generated by social media, blogs, posts, and product reviews, among other places, has become the best-suited examples for consumer sentiment, delivering the best-suited notion for that particular product. As a result, the hybrid feature selection proposed in this paper is a combination of particle swarm optimization (PSO) and cuckoo search. The hybrid feature selection technique surpasses the standard technique due to the subjective nature of social media reviews. Support Vector Machine (SVM) classifier was used to examine performance criteria such as f-measure, recall, precision, and accuracy on the Twitter dataset, and it was compared to a convolution neural network. The proposed work outperforms the existing work, according to the findings of this paper's experiments based on various factors.
Social media has become an important part of our daily lives due to the recent trends. Sites are flooding with tons of posts and opinions of people and social media communication has been on the rise. Although this has mostly been a boon to us, unfortunately it involves enormous dangers, since online texts with high toxicity can cause personal attacks, online harassment and bullying behaviours. People hiding behind closed doors with anonymity can do whatever they want with a keyboard. Unfortunately, not enough means exist to tackle this issue. Recently the employment of Convolutional Neural Networks and Recurrent Neural Networks are approached for computational purposes for the text classification systems. This work utilizes this for finding foul and malicious comments using the Kaggle data set. The work aims to classify a comment into 6 labels of toxicity. This work also implements a completely functional frontend environment built using React JS and MongoDB, which classifies a user entered text into the mentioned labels of toxicity.
This study explored the Influence of School Heads Competence and Qualifications to the School Performance. This quantitative research utilized Descriptive-correlational and determined 48 School heads from Elementary and Secondary schools in Narra District, Narra, Palawan, Philippines. A self-completed questionnaire developed by the researchers was administered to gather the school heads’ profile and school’s performance. Mean, percentage, and standard deviation was used in analyzing the school heads’ profile and the school’s performance while the Pearson correlation was employed to test the relationship of the research’s variables. Results showed that in terms of the profile, majority of the respondents were graduates of bachelor’s degree with units in master’s program and almost half of them have international level of training while more than half were Principal III and have 4 to 7 years of practice as school head. In terms of their performance in the last three years, both in the years 2017-2018 and 2018-2019, the school heads obtained a very satisfactory performance and outstanding performance in the year 2019-2020. Both in the school years 2017-2018 and 2018-2019, the schools in Narra District obtained a maturing level. However, in the school year 2019-2020, the schools in Narra District got a rating, which was described as advanced level. It was found out also that there was no significant relationship between the school heads’ profile and the school performance was established. The result lead to recommendation to strengthen the school-based management areas where the schools obtained a maturing level while sustain the current level of practice in the SBM areas where they obtained an advanced level.
AI in agriculture is a fast-developing technology in this world. It helps farmers to identify the present condition of the field where they are in. There are several applications in AI which helps them to improve the income of their family and their yield. The applications of AI in agriculture are drones, crops, soil monitoring and predictive analysis. This helps in food security concerns forced the innovations and technological advancements in agriculture. In applications of AI in agriculture. The farmers can gain the information about the yield in easier way which can reduce the loss for the farmers by using this we may reduce the loss of the crops. It helps them in autonomous robots to handle essential agricultural tasks easily. And it gives potential invasions and information about the crops. Precision agriculture is used in AI technology is for aid in detecting diseases in plants, pests, and poor plant nutrition.
Data visualization has been rising rapidly for the past a few years in the BI and analytics industry, as part of the modern BI movement which emphasizes on self-service. Secondly, data visualization concepts and guidelines are realized through features in BI software, which makes it easy to apply the concepts. This is similar to the concept of Object-Oriented programming. Object Oriented Programming is a concept that could be implemented in any programming language, such as C. Compared to other types and applications of visualization, business data visualization, particularly concerns about the visualization of business data, is mainly for the purpose of communication, information seeking, analysis, and decision support.
