Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The authors examine the implications of electronic shopping for consumers, retailers, and manufacturers. They assume that near-term technological developments will offer consumers unparalleled opportunities to locate and compare product offerings. They examine these advantages as a function of typical consumer goals and the types of products and services being sought and offer conclusions regarding consumer incentives and disincentives to purchase through interactive home shopping vis-à-vis traditional retail formats. The authors discuss implications for industry structure as they pertain to competition among retailers, competition among manufacturers, and retailer-manufacturer relationships.

Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in

Innovations for sustainable development: Moving toward a sustainable future

Student evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research is to establish an efficient sentiment classification scheme on instructor evaluation reviews by pursuing the paradigm of deep learning. Deep learning is a recent research direction of machine learning, which seeks to identify a classification scheme with higher predictive performance based on multiple layers of nonlinear information processing. In this study, we present a recurrent neural network (RNN) based model for opinion mining on instructor evaluation reviews. We analyze a corpus containing 154,000 such reviews, with the use of conventional machine learning algorithms, ensemble learning methods, and deep learning architectures. In the empirical analysis, three conventional text representation schemes (namely, term‐presence, term‐frequency [TF], and TF‐inverse document frequency schemes) and four word embedding schemes (namely, word2vec, global vector [GloVe], fastText, and LDA2Vec) have been taken into consideration. The predictive performance of supervised machine learning methods (such as, Naïve Bayes, support vector machines, logistic regression, K‐nearest neighbor, and random forest) and three ensemble learning methods have been examined on word embedding schemes. The extensive empirical analysis indicates that deep learning‐based architectures outperform the conventional machine learning classifiers for the task of sentiment classification on instructor reviews. For the RNN with attention mechanism in conjunction with GloVe word embedding scheme‐based representation a classification accuracy of 98.29% has been obtained.

Mining opinions from instructor evaluation reviews: A deep learning approach

Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age‐, gender‐, race‐, or geography‐related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word‐embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods. The empirical analysis indicate that deep learning‐based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short‐term memory networks in conjunction with GloVe word‐embedding scheme‐based representation, with a classification accuracy of 95.80%.

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Mining social network users opinions' to aid buyers' shopping decisions

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).,The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.,The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.,The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

Data-driven Social Mood Analysis through the Conceptualization of Emotional Fingerprints

The aim of this work is to review a specific learning analytics method-sentiment analysis-in the field of Higher Education, showing how it is employed to monitor student satisfaction on different platforms, and to propose an architecture of Sentiment Analysis for Higher Education purposes, which trace and unify what emerges from the literature review. First, a literature review is carried out, which proves the widespread and increasing interest of the communities, of both scholars and practitioners, in the use of sentiment analysis in the field of Higher Education. The analysis, focused on three different e-learning domains, identifies weaknesses and gaps, and in particular the lack of a unifying approach which is able to deal with the different domains. Secondly, a prototype architecture-LADEL Learning Analytics Dashboard for E-Learning-is introduced, which is able to deal with the different e-learning domains. Some preliminary experiments are carried out, highlighting some limitations and open issues, as stimulus to continue the development of the platform.

Sentiment Analysis to Evaluate Teaching Performance

With the growth of online shopping, the buyers are faced with information and cognitive overload, entailing worse buyers' decisions Various decision aids, more and more implemented as web services, aim at reducing this overload Often they implement compensatory strategies that enable desirable and undesirable values of a product attribute to compensate each other However, increasing the number of options beyond a handful can lead to poor choices, decreasing satisfaction (ie, paradox of choice) In such a situation, that involves uncertainty, people relies more on heuristics than rationality to arrive at decisions and purchases Heuristics, or noncompensatory strategies, do not consider a buyer's preference for multiple attributes, such as the satisficing heuristic that compares each attribute value with a predetermined cut-off level, rejecting alternatives that do not meet it This paper presents a study combining an E-Commerce literature survey, an E-Commerce websites' analysis, and a survey of online buyers opinions It is pointing to a gap that exists between sellers' services and buyers' expectations Empirical evidence suggests that it can be bridged turning to noncompensatory strategies implemented as web services

Ernesto D'Avanzo

Papers

Mining social network users opinions' to aid buyers' shopping decisions

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

Data-driven Social Mood Analysis through the Conceptualization of Emotional Fingerprints

Sentiment Analysis to Evaluate Teaching Performance

E-commerce websites services versus buyers expectations: an empirical analysis of the online marketplace