A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

Desai, Mitali; Mehta, Mayuri A.

doi:10.5121/ijdkp.2016.6206

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…If input data comes from SNs, preprocessing requires other several steps, such as online text cleaning (like removing URLs, HTML tags or the Retweets tag [RT]), expanding abbreviation or acronyms, handling or removing emoticons, and replacing or removing repeated characters like the “o” and the “p” and “y” in the following words, “ coooooool” or “ happyyyyyyyyyyyyyyyyy” (Brody & Diakopoulos, ; Desai & Mehta, ; Go, Bhayani, & Huang, ; Haddi, Liu, & Shi, ).…”

Section: Sentiment Analysis Methodsmentioning

confidence: 99%

Sentiment analysis for mining texts and social networks data: Methods and tools

Zucco

Calabrese

Agapito

et al. 2019

WIREs Data Min & Knowl

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Social networks (SNs) represent an established environment in which users share daily emotions and opinions. Therefore, they have become an essential source of big data related to sentiment/opinion sphere. Sentiment analysis (SA) aims to extract sentiments, emotions or opinions from texts, made available by different data sources like SNs. This review presents a depth study relative to the methods and the main tools for SA. The analysis was performed by defining four criteria and several variables to compare 24 tools with objective criteria. Specifically, the tools have been analyzed and tested to verify their usability, flexibility of use, and other specifications related to the type of analysis performed. The majority of tools can detect positive, negative, and neutral polarity, while few tools only detect positive and negative polarity. Moreover, seven tools were able to recognize emotions, and only one provides a visual map for geo‐referenced data. Except for one, remaining 23 tools offer service through the web interface. Finally, only nine tools provide both application program interfaces and a client for common programming languages to allow potential developer end‐users to integrate a specific SA tool into their application. Differently, from other recent surveys, the paper presents and discusses both methods and tools for analyzing texts and SN data sources to extract sentiment. Moreover, it contains a comprehensive comparison with other recent surveys. The comparative analysis of the tools completed according to objective criteria allows to highlight some limits on main tools that need to be faced with enhancing the end‐user experience. This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Data Mining Software Tools

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Section: Sentiment Analysis Methodsmentioning

confidence: 99%

Sentiment analysis for mining texts and social networks data: Methods and tools

Zucco

Calabrese

Agapito

et al. 2019

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…Then, they have evaluated a number of ML approaches and identified those most suitable to classifying public sentiment towards gun violence in light of the Sandy Hook school shooting. (Desai & Mehta, 2016) have proposed a novel Hybrid Classification Algorithm (HCA) for descriptive sentiment analysis to understand students' problems and perks deeply. For this, the authors have integrated both subjective analysis and DM techniques to make the process descriptive.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Menaouer

Zahra

Mohammed

2022

International Journal of Service Science, Management, Engineering, and Technology

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Social media has revolutionized the way people disclose their personal health concerns and express opinions on public health issues. In this paper a new approach for multi-class sentiment classification using supervised learning techniques. The aim of this multi-class sentiment classification is to assign the healthcare Tweets automatically into predetermined categories on the basis of their linguistic characteristics, their contents, and some of the words that characterize each category from the others. Briefly, relevant health datasets are collected from Twitter using Twitter API; then, use of the methodology is illustrated and evaluated against one with only three different algorithms was used, to improve the accuracy of Decision Trees, SMO, and K-NN classifiers. Many experiments accomplished to prove the validity and efficiency of the approach using datasets tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes

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“…Para destacar los trabajos relacionados con el presente estudio, se elaboró una tabla comparativa que permite identificar, por una parte, las tareas de la extracción de datos, las técnicas utilizadas para el análisis de datos, así como el propósito de cada uno. De los 14 estudios, diez de ellos utilizan Twitter como fuente para extraer la información, a través de la búsqueda de términos, hashtags o temas relacionados a problemas de ingeniería (Abdelhamid et al, 2020;Chen et al, 2014;Desai y Mehta, 2016;Greeshma y Veigas, 2020;Hasan et al, 2014;Ingole et al, 2018;Joshi y Sharma, 2021;Patel y Mistry, 2015;Patil y Kulkarni, 2018;Sambhaji, 2021). Tres de ellos tienen como datos de entrada, las huellas digitales de los estudiantes (Azcona et al, 2019;Blikstein, 2011;Blikstein et al, 2014) y un estudio extrae información de un blog (T. Oanh y Thanh, 2017).…”

Section: Introductionunclassified

Predicción de dificultades estudiantiles mediante técnicas de minería de textos

Pérez-Suasnavas¹,

Hasperué²,

Bustamante³

et al. 2022

S. F. J. of Dev.

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Uno de los problemas a los que se enfrenta el docente universitario, se relaciona con la falta de retroalimentación por parte de los estudiantes, que le permita conocer las dificultades que ellos enfrentan a la hora de adquirir conocimientos nuevos en clase. Algunos esfuerzos de investigación se han realizado para identificar y clasificar los problemas de los estudiantes, con datos provenientes de las redes sociales, pero limitando los criterios de búsqueda y recuperación de los datos. El presente artículo pretende cubrir la brecha dejada por los trabajos anteriores, mediante la creación de una metodología para predecir las dificultades de los estudiantes universitarios, que utiliza técnicas de extracción de datos provenientes de la red social Twitter y que ha sido plasmada en un flujo de trabajo, que integra técnicas de Minería de Textos, Procesamiento de Lenguaje Natural y Aprendizaje Profundo, usando Algoritmos Supervisados y Redes Neuronales Recurrentes. Para probar la metodología propuesta, se construyó un modelo de clasificación, cuya evaluación es prometedora, pues se alcanzó una precisión y exactitud del 80%. Los resultados obtenidos garantizan la predicción de las dificultades estudiantiles, dotando al docente de una herramienta efectiva para identificar las áreas que demanden mayor atención y poder así brindar ayuda oportuna a los estudiantes que tienen dificultades en el aula.

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A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

Abstract: ABSTRACT

Cited by 4 publications

References 17 publications

Sentiment analysis for mining texts and social networks data: Methods and tools

Sentiment analysis for mining texts and social networks data: Methods and tools

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Predicción de dificultades estudiantiles mediante técnicas de minería de textos

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