United we stand: improving sentiment analysis by joining machine learning and rule based methods

In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction. 1

Features and machine learning classification of connected speech samples from patients with autopsy proven Alzheimer's disease with and without additional vascular pathology.

Mixed vascular and Alzheimer-type dementia and pure Alzheimer's disease are both associated with changes in spoken language. These changes have, however, seldom been subjected to systematic comparison. In the present study, we analyzed language samples obtained during the course of a longitudinal clinical study from patients in whom one or other pathology was verified at post mortem. The aims of the study were twofold: first, to confirm the presence of differences in language produced by members of the two groups using quantitative methods of evaluation; and secondly to ascertain the most informative sources of variation between the groups. We adopted a computational approach to evaluate digitized transcripts of connected speech along a range of language-related dimensions. We then used machine learning text classification to assign the samples to one of the two pathological groups on the basis of these features. The classifiers' accuracies were tested using simple lexical features, syntactic features, and more complex statistical and information theory characteristics. Maximum accuracy was achieved when word occurrences and frequencies alone were used. Features based on syntactic and lexical complexity yielded lower discrimination scores, but all combinations of features showed significantly better performance than a baseline condition in which every transcript was assigned randomly to one of the two classes. The classification results illustrate the word content specific differences in the spoken language of the two groups. In addition, those with mixed pathology were found to exhibit a marked reduction in lexical variation and complexity compared to their pure AD counterparts

Investigating metaphorical language in sentiment analysis: A sense-to-sentiment perspective

Intuition dictates that figurative language and especially metaphorical expressions should convey sentiment. It is the aim of this work to validate this intuition by showing that figurative language (metaphors) appearing in a sentence drive the polarity of that sentence. Towards this target, the current article proposes an approach for sentiment analysis of sentences where figurative language plays a dominant role. This approach applies Word Sense Disambiguation aiming to assign polarity to word senses rather than tokens. Sentence polarity is determined using the individual polarities for metaphorical senses as well as other contextual information. Experimental evaluation shows that the proposed method achieves high scores in comparison with other state-of-the-art approaches tested on the same corpora. Finally, experimental results provide supportive evidence that this method is also well suited for corpora consisting of literal and figurative language sentences. © 2012 ACM

Sudden unexplained death in the young: Epidemiology, aetiology and value of the clinically guided genetic screening

Aims To determine the incidence and the causes of sudden death (SD) in persons aged 1-35 years old and the diagnostic yield of clinically guided genetic screening in the sudden arrhythmic death syndrome (SADS) victims' families. Methods and results Incidence and causes of SD in the Attica region of Greece in 2002-10 were determined using death certificates and autopsy reports. We evaluated clinically consecutive families of SADS victims and if a clinical diagnosis was established, we proceeded to targeted genetic analysis. Out of 6030 deaths, 56% were due to traumatic or violent causes, 40.5% were natural deaths, and 3.3% were of undetermined cause. There were 349 SD cases. Cardiovascular causes accounted for 65%, non-cardiovascular causes for 17%, and SADS for 18%. Clinical evaluation identified an inherited heart disease in 5/20 SADS families (25%). Targeted genetic analysis identified a causative mutation in all of the five screened families and reconfirmed the diagnosis in three of five proband victims. Clinical and genetic evaluation of 28 family members identified eight affected carriers and eight non-affected carriers. Molecular autopsy failed to identify any of these families. Conclusion Sudden death in the young is of cardiovascular origin in the majority of cases. A considerable rate of SD cases remains of unknown cause on post-mortem. Apart from channelopathies, subclinical forms of inherited structural heart diseases would appear to be implicated in SADS. Clinically guided genetic screening has a significant diagnostic yield and identifies affected families that would have been missed by the current suggested molecular autopsy panel. © The Author 2017

Public Policy Formulation through Non Moderated Crowdsourcing in Social Media

Part 5: Techniques and AnalysisInternational audienceThe emergence of web 2.0 social media enables the gradual emergence of a second generation of e-participation characterized by more citizens’ control, in which government agencies post content (e.g. short or longer text, images, video) to various social media and then analyze citizens’ interactions with it (e.g. views, likes/dislikes, comments, etc.). In this paper we propose an even more citizens controlled third generation of e-participation exploiting web 2.0 social media as well, but in a different manner. It is based on the search by government agencies for content on a public policy under formulation, which has been created in a large set of web 2.0 sources (e.g. blogs and microblogs, news sharing sites, online forums) by citizens freely, without any initiation, stimulation or moderation through government postings. This content undergoes advanced processing in order to extract from it arguments, opinions, issues and proposals on the particular policy, identify their sentiments (positive or negative), and finally summarize and visualize them. This approach allows the exploitation of the vast amount of user-generated content created in numerous web 2.0 social media for supporting governments to understand better the needs, wishes and beliefs of citizens, and create better and more socially rooted policies