3 research outputs found
Analysis of Students Emotion for Twitter Data using Naïve Bayes and Non Linear Support Vector Machine Approachs
Students' informal discussions on social media (e.g Twitter, Facebook) shed light into their educational understandings- opinions, feelings, and concerns about the knowledge process. Data from such surroundings can provide valuable knowledge about students learning. Examining such data, however can be challenging. The difficulty of students' experiences reflected from social media content requires human analysis. However, the growing scale of data demands spontaneous data analysis techniques. The posts of engineering students' on twitter is focused to understand issues and problems in their educational experiences. Analysis on samples taken from tweets related to engineering students' college life is conducted. The proposed work is to explore engineering students informal conversations on Twitter in order to understand issues and problems students encounter in their learning experiences. The encounter problems of engineering students from tweets such as heavy study load, lack of social engagement and sleep deprivation are considered as labels. To classify tweets reflecting students' problems multi-label classification algorithms is implemented. Non Linear Support Vector Machine, Naïve Bayes and Linear Support Vector Machine methods are used as multilabel classifiers which are implemented and compared in terms of accuracy. Non Linear SVM has shown more accuracy than Naïve Bayes classifier and linear Support Vector Machine classifier. The algorithms are used to train a detector of student problems from tweets.
DOI: 10.17762/ijritcc2321-8169.150515
Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
Building trustworthy knowledge graphs for cyber-physical social systems
(CPSS) is a challenge. In particular, current approaches relying on human
experts have limited scalability, while automated approaches are often not
accountable to users resulting in knowledge graphs of questionable quality.
This paper introduces a novel pervasive knowledge graph builder that brings
together automation, experts' and crowd-sourced citizens' knowledge. The
knowledge graph grows via automated link predictions using genetic programming
that are validated by humans for improving transparency and calibrating
accuracy. The knowledge graph builder is designed for pervasive devices such as
smartphones and preserves privacy by localizing all computations. The accuracy,
practicality, and usability of the knowledge graph builder is evaluated in a
real-world social experiment that involves a smartphone implementation and a
Smart City application scenario. The proposed knowledge graph building
methodology outperforms the baseline method in terms of accuracy while
demonstrating its efficient calculations on smartphones and the feasibility of
the pervasive human supervision process in terms of high interactions
throughput. These findings promise new opportunities to crowd-source and
operate pervasive reasoning systems for cyber-physical social systems in Smart
Cities