Ensemble deep learning for multilabel binary classification of user-generated content

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5% to 5.4%

A multivalued emotion lexicon created and evaluated by the crowd

Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to perform emotion analysis. Unsupervised emotion analysis methods require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the emotional range and diversity captured. Emotion analysis lexicons are created manually by domain experts and usually assign one single emotion to each word. We propose an automated workflow for creating and evaluating a multi- valued emotion lexicon created and evaluated through crowdsourcing. We compare the obtained lexicon with established lexicons and appoint expert English Linguists to assess crowd peer-evaluations. The proposed workflow provides a quality lexicon and can be used in a range of text property association tasks

A multivalued emotion lexicon created and evaluated by the crowd

Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to perform emotion analysis. Unsupervised emotion analysis methods require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the emotional range and diversity captured. Emotion analysis lexicons are created manually by domain experts and usually assign one single emotion to each word. We propose an automated workflow for creating and evaluating a multi- valued emotion lexicon created and evaluated through crowdsourcing. We compare the obtained lexicon with established lexicons and appoint expert English Linguists to assess crowd peer-evaluations. The proposed workflow provides a quality lexicon and can be used in a range of text property association tasks

G protein-coupled receptors at the crossroad between physiologic and pathologic angiogenesis : old paradigms and emerging concepts

G protein-coupled receptors (GPCRs) have been implicated in transmitting signals across the extra- and intra-cellular compartments, thus allowing environmental stimuli to elicit critical biological responses. As GPCRs can be activated by an extensive range of factors including hormones, neurotransmitters, phospholipids and other stimuli, their involvement in a plethora of physiological functions is not surprising. Aberrant GPCR signaling has been regarded as a major contributor to diverse pathologic conditions, such as inflammatory, cardiovascular and neoplastic diseases. In this regard, solid tumors have been demonstrated to activate an angiogenic program that relies on GPCR action to support cancer growth and metastatic dissemination. Therefore, the manipulation of aberrant GPCR signaling could represent a promising target in anticancer therapy. Here, we highlight the GPCR-mediated angiogenic function focusing on the molecular mechanisms and transduction effectors driving the patho-physiological vasculogenesis. Specifically, we describe evidence for the role of heptahelic receptors and associated G proteins in promoting angiogenic responses in pathologic conditions, especially tumor angiogenesis and progression. Likewise, we discuss opportunities to manipulate aberrant GPCR-mediated angiogenic signaling for therapeutic benefit using innovative GPCR-targeted and patient-tailored pharmacological strategies

Antithrombotic therapy and survival in patients with malignant disease

A broad range of studies suggest a two-way relationship between cancer and venous thromboembolism (VTE). Patients with cancer have consistently been shown to be at elevated risk for VTE; this risk is partly driven by an intrinsic hypercoagulable state elicited by the tumour itself. Conversely, thromboembolic events in patients without obvious risk factors are often the first clinical manifestation of an undiagnosed malignancy. The relationship between VTE and cancer is further supported by a number of trials and meta-analyses which, when taken together, strongly suggest that antithrombotic therapy can extend survival in patients with cancer by a mechanism that extends beyond its effect in preventing VTE. Moreover, accumulating evidence from in vitro and in vivo studies has shown that tumour growth, invasion, and metastasis are governed, in part, by elements of the coagulation system. On 22 May 2009, a group of health-care providers based in the United Kingdom met in London, England, to examine recent advances in cancer-associated thrombosis and its implications for UK clinical practice. As part of the discussion, attendees evaluated evidence for and against an effect of antithrombotic therapy on survival in cancer. This paper includes a summary of the data presented at the meeting and explores potential mechanisms by which antithrombotic agents might exert antitumour effects. The summary is followed by a consensus statement developed by the group

Sociolinguistics of Style and Social Class in Contemporary Athens

This ethnographic study deals with the ways people in Athens, Greece, use style to construct their social class identities. Including a rich dataset comprising ethnographic interviews with actual people who live in the stereotypically seen as leafy and posh northern suburbs and in the stereotypically treated as working class western suburbs of Athens coupled with data from popular literary novels, TV series and Greek hip hop music, it argues that the relationship between style and social class identity is mediated by complex social meanings encompassing features from and discourses relevant to both areas, which are structured across different orders of indexicality depending on the genre of speech in which they are created. As such, it will be of interest to scholars in sociolinguistics, discourse analysis, anthropology, sociology, Modern Greek studies, and to everyone who is interested in how social class is constructed via language.Greek State Scholarships Foundation, Onassis Public Benefit Fund, Foundation for Education and European Cultur

Emerging Roles of PAR-1 and PAFR in Melanoma Metastasis

Melanoma growth, angiogenesis and metastatic progression are strongly promoted by the inflammatory tumor microenvironment due to high levels of cytokine and chemokine secretion by the recruited inflammatory and stromal cells. In addition, platelets and molecular components of procoagulant pathways have been recently emerging as critical players of tumor growth and metastasis. In particular, thrombin, through the activity of its receptor protease-activated receptor-1 (PAR-1), regulates tumor cell adhesion to platelets and endothelial cells, stimulates tumor angiogenesis, and promotes tumor growth and metastasis. Notably, in many tumor types including melanoma, PAR-1 expression directly correlates with their metastatic phenotype and is directly responsible for the expression of interleukin-8, matrix metalloproteinase-2 (MMP-2), vascular endothelial growth factor, platelet-derived growth factor, and integrins. Another proinflammatory receptor–ligand pair, platelet-activating factor (PAF) and its receptor (PAFR), have been shown to act as important modulators of tumor cell adhesion to endothelial cells, angiogenesis, tumor growth and metastasis. PAF is a bioactive lipid produced by a variety of cells from membrane glycerophospholipids in the same reaction that releases arachidonic acid, and can be secreted by platelets, inflammatory cells, keratinocytes and endothelial cells. We have demonstrated that in metastatic melanoma cells, PAF stimulates the phosphorylation of cyclic adenosine monophosphate response element-binding protein (CREB) and activating transcription factor 1 (ATF-1), which results in overexpression of MMP-2 and membrane type 1-MMP (membrane type 1-MMP). Since only metastatic melanoma cells overexpress CREB/ATF-1, we propose that metastatic melanoma cells are better equipped than their non-metastatic counterparts to respond to PAF within the tumor microenvironment. The evidence supporting the hypothesis that the two G-protein coupled receptors, PAR-1 and PAFR, contribute to the acquisition of the metastatic phenotype of melanoma is presented and discussed