Aspect Based Sentiment Analysis using Various Supervised Classification Techniques: An Overview

The Sentiment Analysis (SA) work is concerned with identifying aspect terms and categories and categorising emotions (positive, negatively, conflict, and neutral) in ratings and reviews. When it comes to subjectivity, it's typical to divide sentences into objective phrases that include accurate information and subjective statements that include express ideas, beliefs, and perspectives on a given topic. Various existing researchers have already done a lot of work in sentiment analysis with various methods, including aspect extraction. This paper proposed a systematic literature analysis of numerous sentiment analysis using supervised and unsupervised classification techniques. We investigate a few features extraction Natural language Processing (NLP) techniques used to identify aspects of machine learning for the detection of sentiment. An extensive experiment analysis, we discuss the findings of the study, challenges of the current and define the problem statement for the future directio

The Impact of Cultural Familiarity on Students’ Social Media Usage in Higher Education

Using social media (SM) in Higher education (HE) becomes unavoidable in the new teaching and learning pedagogy. The current generation of students creates their groups on SM for collaboration. However, SM can be a primary source of learning distraction due to its nature, which does not support structured learning. Hence, derived from the literature, this study proposes three learning customised system features, to be implemented on SM when used in Higher Education HE. Nevertheless, some psychological factors appear to have a stronger impact on students’ adoption of SM in learning than the proposed features. A Quantitative survey was conducted at a university in Uzbekistan to collect 52 undergraduate students’ perception of proposed SM learning customised features in Moodle. These features aim to provide localised, personalised, and privacy control self-management environment for collaboration in Moodle. These features could be significant in predicting students’ engagement with SM in HE. The data analysis showed a majority of positive feedback towards the proposed learning customised SM. However, the surveyed students’ engagement with these features was observed as minimal. The course leader initiated a semi-structured interview to investigate the reason. Although the students confirmed their acceptance of the learning customised features, their preferences to alternate SM, which is Telegram overridden their usage of the proposed learning customized SM, which is Twitter. The students avoided the Moodle integrated Twitter (which provided highly accepted features) and chose to use the Telegram as an external collaboration platform driven by their familiarity and social preferences with the Telegram since it is the popular SM in Uzbekistan. This study is part of an ongoing PhD research which involves deeper frame of learners’ cognitive usage of the learning management system. However, this paper exclusively discusses the cultural familiarity impact of student’s adoption of SM in HE

Analyzing Public Sentiments: A Review

Large number of users share their opinions on Social networking sites. it can be useful for analyzing sentiments of different peoples about different domains/products. so that this analysis can be beneficial for making various decisions in various fields. For instance a company can analyze sentiments about products while a politician can view comments about them to improve their position in the society. in previous studies only track of sentiments can be taken but in this we are trying to analyze the public sentiments and trying to find out the possible reasons behind variation about comments based on that we tries to propose a system and tries to improve performance of system. DOI: 10.17762/ijritcc2321-8169.15021

Polarity of opinions about a public person in Ecuador

The present investigation is the study of opinion miningtechniques, focused on obtaining information from a public figurein Ecuador, determining signs of polarity for your management ina positive, negative or neutral way, a result that will allow saidcharacter public to make decisions about their actions based on animage of service to the community. The extraction of opinions insocial networks and techniques based on Human LanguageTechnologies enabled the interpretation of polarized data byspecifying parameters of relevance to the resulting opinion focusedon decision making, processing that adapts to the newcommunication formats achieving the interpretation andassessment of opinion. Social networks was the platform for thecapture of texts by means of an API, which after the processing ofthe natural language obtained results of indications of thepopularity of the character

Big Data Mining and Semantic Technologies: Challenges and Opportunities

Big data a term coined due to the explosion in the quantity and diversity of high frequency digital data which is having a potential for valuable insights has drawn the most attention in the area of research and development. Converting big data to actionable insights requires depth understanding of big data, its characteristics, challenges and current technological trends. A rise of big data is changing the existing data storage, management, processing and analytical mechanisms and leads to the new architecture/ecosystems to handle big data applications. This paper covers finding of our research study about big data characteristic, various types of analysis associated with it and basic big data types. First, we are presenting the big data study from data mining and analysis perspective and discuss the challenges and next, we present the result of research study on meaningful use of big data in the context of semantic technologies. Moreover, we discuss various case studies related to social media analysis and recent development trends to identify potential research directions for big data with semantic technologies. DOI: 10.17762/ijritcc2321-8169.150711

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

Twitter y aprendizaje en la universidad: análisis de la producción científica en Scopus

Twitter offers learning opportunities through interaction between educational actors and also it allows sharing of several resources and materials, it having great relevance and impact on higher education. Therefore, the aim of this paper is to analyse bibliometrically the scientific production on the use of Twitter in learning processes in the university context. The final sample, extracted from the Scopus database, consists of 248 articles published between 2012 and 2021, and it is examined using various bibliometric techniques (bibliographic linking, co-citation and co-occurrence). The results confirm a progressive growth in scientific production, mainly indexed in the social sciences area, with the United States being the most prolific country and the UNED the institution with the highest scientific production. The most cited articles are focused on analysing of the potential of Twitter in higher education and the use that academics and university institutions make of this social network. Future lines of development include the influence of Twitter on factors such as academic performance and motivation. In conclusion, stand out the relevance of the study of scientific production in order to generate new didactic proposals and to develop research that scientifically support the benefits of incorporating Twitter into training processesTwitter ofrece oportunidades de aprendizaje mediante la interacción entre agentes educativos y permite compartir diferentes recursos y materiales. Por ello, el objetivo de este trabajo es analizar bibliométricamente la producción científica sobre el uso de Twitter en los procesos de aprendizaje en el contexto universitario. La muestra final, extraída de la base de datos Scopus, la componen 248 artículos publicados entre 2012 y 2021, examinada con varias técnicas bibliométricas (acoplamiento bibliográfico, co-citación y co-ocurrencia). Los resultados confirman un crecimiento progresivo de la producción científica, indexada principalmente en ciencias sociales, siendo Estados Unidos el país más prolífico y la UNED la institución con mayor producción científica. Los artículos más citados se centran en analizar el potencial de Twitter en la educación superior y el uso que hacen los académicos y las instituciones universitarias de esta red social. Entre las futuras líneas de desarrollo, destaca los diferentes tipos de aprendizaje que pueden desarrollarse utilizando Twitter como recurso, así como su influencia en factores como el rendimiento académico y la motivación. Como conclusión, destacar la relevancia del estudio de la producción científica para generar nuevas propuestas didácticas e investigaciones que fundamenten científicamente los beneficios de incorporar Twitter a los procesos formativo

An Infectious Disease Prediction Method Based on K-Nearest Neighbor Improved Algorithm

With the continuous development of medical information construction, the potential value of a large amount of medical information has not been exploited. Excavate a large number of medical records of outpatients, and train to generate disease prediction models to assist doctors in diagnosis and improve work efficiency.This paper proposes a disease prediction method based on k-nearest neighbor improvement algorithm from the perspective of patient similarity analysis. The method draws on the idea of clustering, extracts the samples near the center point generated by the clustering, applies these samples as a new training sample set in the K-nearest neighbor algorithm; based on the maximum entropy The K-nearest neighbor algorithm is improved to overcome the influence of the weight coefficient in the traditional algorithm and improve the accuracy of the algorithm. The real experimental data proves that the proposed k-nearest neighbor improvement algorithm has better accuracy and operational efficiency

Mining Students’ Messages to Discover Problems Associated with Academic Learning

WhatsApp has become the preferred choice of students for sending messages in developing countries. Due to its privacy and the ability to create groups, students are able to express their “feelings” to peers without fear. To obtain immediate feedback on problems hindering effective learning, supervised learning algorithms were applied to mine the sentiments in WhatsApp group messages of University students. An ensemble classifier made up of Naïve Bayes, Support Vector Machines, and Decision Trees outperformed the individual classifiers in predicting the mood of students with an accuracy of 0.76, 0.92 recall, 0.72 precision and 0.80 F-score. These results show that we can predict the mood and emotions of students towards academic learning from their private messages. The method is therefore proposed as one of the effective ways by which educational authorities can cost effectively monitor issues hindering students’ academic learning and by extension their academic progress. Keywords: WhatsApp; Sentiments; Ensemble; Classification; Naïve Bayes; Support Vector Machines.