Data Mining Practical Machine Learning Tools and Techniques

Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count

During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.

Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

How blockchain improves the supply chain: case study alimentary supply chain

Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naive Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).

A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

A polarity analysis framework for Twitter messages

2014 Special Issue: A multi-label, semi-supervised classification approach applied to personality prediction in social media

Twitter® is a microblogging service usually used as an instant communication platform. The capacity to provide information in real time has stimulated many companies to use this service to understand their consumers. In this direction, TV stations have adopted Twitter for shortening the distance between them and their viewers, and use such information as a feedback mechanism for their shows. The sentiment analysis task can be used as one such feedback mechanism. This task corresponds to classifying a text according to the sentiment that the writer intended to transmit. A classifier usually requires a pre-classifled data sample to determine the class of new data. Typically, the sample is pre-classified manually, making the process time consuming and reducing its real time applicability for big data. This paper proposes an automatic sentiment classifier for Twitter messages, and uses TV shows from Brazilian stations for benchmarking. The automatic sentiment analysis reduces human intervention and, thus, the complexity and cost of the whole process. To assess the performance of the proposed system tweets related to a Brazilian TV show were captured in a 24h interval and fed into the system. The proposed technique achieved an average accuracy of 90%.

Automatic sentiment analysis of Twitter messages

Social media allow web surfers to produce and share content about different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data analysis researchers seeking to infer behaviors and trends, besides creating statistics involving social sites. A possible research involving these data is known as personality analysis, which aims to understand the user's behavior in a social media. Thus, this paper uses machine learning techniques to predict personality traits in groups of tweets. In traditional approaches of personality prediction the analysis is made in the users' profiles and their tweets. However, in this paper the approach arises from the fact that the personality analysis is performed in groups of tweets. The prediction is based on the Big Five Model, also called Five Factor Model, which divides personality traits into five dimensions and uses linguistic information to identify these traits. This paper uses TV shows from Brazilian stations for benchmarking. The system achieved an average accuracy of 84%.

Multi-label Semi-supervised Classification Applied to Personality Prediction in Tweets

Temperament is an innate psychological characteristic associated with how we relate with the world. This feature is often used to direct careers, manage conflicts, develop leadership, improve teaching, etc. The data generated by social media users represent user behavior facing the various situations of everyday life. With this, machine learning techniques can be used to infer the tem-perament, as is already done in the vast research on sentiment analysis and the growing research on personality prediction. This paper proposes a framework for temperament classification according to the theory of psychologist David Keirsey. Our results present an accuracy higher than 70% for the Artisan and Guardian types.

Ana Carolina E. S. Lima

Papers

A polarity analysis framework for Twitter messages

2014 Special Issue: A multi-label, semi-supervised classification approach applied to personality prediction in social media

Automatic sentiment analysis of Twitter messages

Multi-label Semi-supervised Classification Applied to Personality Prediction in Tweets

Predicting Temperament from Twitter Data