Data Mining Practical Machine Learning Tools and Techniques

The measurement of meaning

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

The Creative Mind: Myths and Mechanisms.

There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.

Sentiment of Emojis.

Stream-based active learning for sentiment analysis in the financial domain

The application addressed in this paper studies whether Twitter feeds, expressing public opinion concerning companies and their products, are a suitable data source for forecasting the movements in stock closing prices. We use the term predictive sentiment analysis to denote the approach in which sentiment analysis is used to predict the changes in the phenomenon of interest. In this paper, positive sentiment probability is proposed as a new indicator to be used in predictive sentiment analysis in finance. By using the Granger causality test we show that sentiment polarity (positive and negative sentiment) can indicate stock price movements a few days in advance. Finally, we adapted the Support Vector Machine classification mechanism to categorize tweets into three sentiment categories (positive, negative and neutral), resulting in improved predictive power of the classifier in the stock market application.

Predictive Sentiment Analysis of Tweets: A Stock Market Application

A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops

Sentiment analysis from data streams is aimed at detecting authors’ attitude, emotions and opinions from texts in real-time. To reduce the labeling effort needed in the data collection phase, active learning is often applied in streaming scenarios, where a learning algorithm is allowed to select new examples to be manually labeled in order to improve the learner’s performance. Even though there are many on-line platforms which perform sentiment analysis, there is no publicly available interactive on-line platform for dynamic adaptive sentiment analysis, which would be able to handle changes in data streams and adapt its behavior over time. This paper describes ClowdFlows, a cloud-based scientific workflow platform, and its extensions enabling the analysis of data streams and active learning. Moreover, by utilizing the data and workflow sharing in ClowdFlows, the labeling of examples can be distributed through crowdsourcing. The advanced features of ClowdFlows are demonstrated on a sentiment analysis use case, using active learning with a linear Support Vector Machine for learning sentiment classification models to be applied to microblogging data streams.

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Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform

DEX is a qualitative multi-attribute decision modeling methodology that integrates multi-criteria decision modeling with rule-based expert systems. The method was conceived in 1979. Since, it has been continuously developed and implemented in a wide range of computer programs that have been applied in hundreds of practical decision-making studies. Here we present its main methodological concepts, contributions to the theory and practice of decision support, and outline a history of its development and evolution.

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Martin Žnidaršič

Papers

Stream-based active learning for sentiment analysis in the financial domain

Predictive Sentiment Analysis of Tweets: A Stock Market Application

A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops

Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform

DEX Methodology: Three Decades of Qualitative Multi-Attribute Modeling