How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science

Amato, Giuseppe; Candela, Leonardo; Castelli, Donatella; Esuli, Andrea; Falchi, Fabrizio; Gennaro, Claudio; Giannotti, Fosca; Monreale, Anna; Nanni, Mirco; Pagano, Pasquale; Pappalardo, Luca; Pedreschi, Dino; Pratesi, Francesca; Rabitti, Fausto; Rinzivillo, Salvatore; Rossetti, Giulio; Ruggieri, Salvatore; Sebastiani, Fabrizio; Tesconi, Maurizio

doi:10.1007/978-3-319-61893-7_17

Cited by 9 publications

(3 citation statements)

References 66 publications

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“…Sentiment analysis is one of the widely researched areas of natural language processing. The sentiment analysis has vast applications in different fields of life such as social science, political sciences [2,3], and marketing [4], in e-commerce and for customer satisfaction [5,6]. Sentiment based analysis of online reviews regarding products and services also helps in understanding satisfaction of customers [7].…”

Section: Introductionmentioning

confidence: 99%

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

et al. 2020

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In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

et al. 2020

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“…The products of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. All these aspects are producing a change in the scientific method, in research and in the way our society makes decisions [2].Data science emerges to concurring facts: (i) the advent of big data that provides the critical mass of actual exam-…”

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confidence: 99%

“…There is an entire discipline of bioinformatics that is devoted to the analysis of such data. 2 Data-centric approaches based on personal behaviors can also support medical applications analyzing data at both human behavior levels and lower molecular ones. For example, integrating genome data of medical reactions with the habits of the users, enabling a computational drug science for high-precision personalized medicine.…”

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confidence: 99%

Data science: a game changer for science and innovation

Grossi

Giannotti

Pedreschi

et al. 2021

Int J Data Sci Anal

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This paper shows data science’s potential for disruptive innovation in science, industry, policy, and people’s lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.

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Distributed Community Prediction for Social Graphs Based on Louvain Algorithm

Makris

Pettas

Pispirigos

2019

IFIP Advances in Information and Communication Technology

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Nowadays, the problem of community detection has become more and more challenging. With application in a wide range of fields such as sociology, digital marketing, bio-informatics, chemical engineering and computer science, the need for scalable and efficient solutions is strongly underlined. Especially, in the rapidly developed and widespread area of social media where the size of the corresponding networks exceeds the hundreds of millions of vertices in the average case. However, the standard sequential algorithms applications have practically proven not only infeasible but also terribly unscalable due to the excessive computation demands and the overdone resources prerequisites. Therefore, the introduction of compatible distributed machine learning solutions seems the most promising option to tackle this NP-hard class problem. The purpose of this work is to propose a novel distributed community detection methodology, based on the supervised community prediction concept that is extremely scalable, remarkably efficient and circumvent the intrinsic adversities of classic community detection approaches.

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How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science

Cited by 9 publications

References 66 publications

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

Data science: a game changer for science and innovation

Distributed Community Prediction for Social Graphs Based on Louvain Algorithm

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