There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Sentiment analysis algorithms and applications: A survey

Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.

Affective Computing and Sentiment Analysis

コンピュータ・サイエンス : ACM computing surveys

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. However, there is a lack of publicly available data for doing research on the interaction between language and depression. In this paper, we describe our first steps to fill this gap. We outline the methodology we have adopted to build and make publicly available a test collection on depression and language use. The resulting corpus includes a series of textual interactions written by different subjects. The new collection not only encourages research on differences in language between depressed and non-depressed individuals, but also on the evolution of the language use of depressed individuals. Further, we propose a novel early detection task and define a novel effectiveness measure to systematically compare early detection algorithms. This new measure takes into account both the accuracy of the decisions taken by the algorithm and the delay in detecting positive cases. We also present baseline results with novel detection methods that process users’ interactions in different ways.

A Test Collection for Research on Depression and Language Use

This paper provides an overview of eRisk 2017. This was the first year that this lab was organized at CLEF. The main purpose of eRisk was to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. The first edition of eRisk included a pilot task on early risk detection of depression.

eRISK 2017: CLEF Lab on Early Risk Prediction on the Internet: Experimental Foundations

This paper provides an overview of eRisk 2019, the third edition of this lab under the CLEF conference. The main purpose of eRisk is to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. This edition of eRisk had three tasks. Two of them shared the same format and focused on early detecting signs of depression (T1) or self-harm (T2). The third task focused on an innovative challenge related to automatically filling a depression questionnaire based on user interactions in social media.

Overview of eRisk 2019 Early Risk Prediction on the Internet

This paper provides an overview of eRisk 2018. This was the second year that this lab was organized at CLEF. The main purpose of eRisk was to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. The second edition of eRisk had two tasks: a task on early risk detection of depression and a task on early risk detection of anorexia.

Overview of eRisk: Early Risk Prediction on the Internet

/pdf/overview-of-erisk-early-risk-prediction-on-the-internet-8lrc6jxw0n.pdf

David E. Losada

Papers

A Test Collection for Research on Depression and Language Use

eRISK 2017: CLEF Lab on Early Risk Prediction on the Internet: Experimental Foundations

Overview of eRisk 2019 Early Risk Prediction on the Internet

Overview of eRisk: Early Risk Prediction on the Internet

Overview of eRisk: Early Risk Prediction on the Internet (Extended Lab Overview).