The Role of Text Pre-processing in Sentiment Analysis

It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature

Capacidade de paras¡tismo de Telenomus podisi em ovos de Euschistus heros tratados com ¡nseticidas.

Conteúdo do volume 2: Ácaros; Biologia, fisiologia, morfologia; Controle biológico com bactérias entomopatogênicas; Controle biológico com fungos entomopatológicos; Controle biológico com nematoides; Controle biológico com parasitoides; Controle biológico com predadores; Ecologia e biodiversidade; Educação e etnoentomologia; Entomologia florestal; Entomologia Forense; Entomologia médica e veterinária; Entomologia molecular; Manejo integrado de pragas; Organismos geneticamente modificados; Plantas inseticidas; Polinização; Pragas quarentenárias e invasivas; Resistência de insetos a táticas de controle; Resistência de plantas a insetos; Semioquímicos e comportamento; Sistemática e taxonomia; Tecnologia de aplicação; Controle biológico com vírus entomopatogênicos; Controle químico

Aerosol-Assisted CVD-Grown PdO Nanoparticle-Decorated Tungsten Oxide Nanoneedles Extremely Sensitive and Selective to Hydrogen

We report for the first time the successful synthesis of palladium (Pd) nanoparticle (NP)-decorated tungsten trioxide (WO3) nanoneedles (NNs) via a two-step aerosol-assisted chemical vapor deposition approach. Morphological, structural, and elemental composition analysis revealed that a Pd(acac)2 precursor was very suitable to decorate WO3 NNs with uniform and well-dispersed PdO NPs. Gas-sensing results revealed that decoration with PdO NPs led to an ultrasensitive and selective hydrogen (H2) gas sensor (sensor response peaks at 1670 at 500 ppm of H2) with low operating temperature (150 °C). The response of decorated NNs is 755 times higher than that of bare WO3 NNs. Additionally, at a temperature near that of the ambient temperature (50 °C), the response of this sensor toward the same concentration of H2 was 23, which is higher than that of some promising sensors reported in the literature. Finally, humidity measurements showed that PdO/WO3 sensors displayed low-cross-sensitivity toward water vapor, compared to bare WO3 sensors. The addition of PdO NPs helps to minimize the effect of ambient humidity on the sensor response

Self‐reported drug allergy in a general adult Portuguese population

Clin Exp Allergy. 2004 Oct;34(10):1597-601. Self-reported drug allergy in a general adult Portuguese population. Gomes E, Cardoso MF, Praça F, Gomes L, Mariño E, Demoly P. Serviço de Imunoalergologia, Hospital Maria Pia, Porto, Portugal. [email protected] Abstract AIM: To estimate the prevalence of self-reported drug allergy in adults. METHODS: Cross-sectional survey of a general adult population from Porto (all of whom were living with children involved in the International Study of Asthma and Allergies in Childhood-phase three), during the year 2002, using a self-administered questionnaire. RESULTS: The prevalence of self-reported drug allergy was 7.8% (181/2309): 4.5% to penicillins or other beta-lactams, 1.9% to aspirin or other non-steroidal anti-inflammatory drugs (NSAIDs) and 1.5% to other drugs. In the group 'allergic to beta-lactams', the most frequently implicated drug was penicillin G or V (76.2%) followed by the association of amoxicillin and clavulanic acids (14.3%). In the group 'allergic to NSAIDs', acetylsalicylic acid (18.2%) and ibuprofen (18.2%) were the most frequently identified drugs, followed by nimesulide and meloxicam. Identification of the exact name of the involved drug was possible in less than one-third of the patients, more often within the NSAID group (59.5%). Women were significantly more likely to claim a drug allergy than men (10.2% vs. 5.3%). The most common manifestations were cutaneous (63.5%), followed by cardiovascular symptoms (35.9%). Most of the reactions were immediate, occurring on the first day of treatment (78.5%). Only half of the patients were submitted to drug allergy investigations. The majority (86.8%) completely avoided the suspected culprit drug thereafter. CONCLUSIONS: The results showed that self-reported allergy to drugs is highly prevalent and poorly explored. Women seem to be more susceptible. beta-lactams and NSAIDs are the most frequently concerned drugs. PMID: 15479276 [PubMed - indexed for MEDLINE

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.This work was co-financed by FCT/MEC and FEDER under Program PT2020 (Project UID/EQU/50020/2013); by Fundacao para a Ciencia e Tecnologia under the strategic funding of UID/BIO/04469/2013 unit; and by Project POCTEP through Project RED/AGROTEC-Experimentation network and transfer for development of agricultural and agro industrial sectors between Spain and Portugal

Towards actionable knowledge: A systematic analysis of mobile patient portal use

As the aging population grows, chronic illness increases, and our healthcare costs sharply increase, patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. A patient’s engagement in their healthcare contributes to improving health outcomes, and information technologies can support health engagement. In this chapter, we extend the existing literature by discovering design gaps for patient portals from a systematic analysis of negative users’ feedback from the actual use of patient portals. Specifically, we adopt a topic modeling approach, latent Dirichlet allocation (LDA) algorithm, to discover design gaps from online low rating user reviews of a common mobile patient portal, EPIC’s mychart. To validate the extracted gaps, we compared the results of LDA analysis with that of human analysis. Overall, the results revealed opportunities to improve collaboration and to enhance the design of portals intended for patient-centered care. Incorporating these changes may enable the technologies to have stronger position to influence health improvement and wellness

Durability of Silicate Glasses: An Historical Approach

We present a short review of current theories of glass weathering, including glass dissolution, and hydrolysis of nuclear waste glasses, and leaching of historical glasses from an XAFS perspective. The results of various laboratory leaching experiments at different timescales (30 days to 12 years) are compared with results for historical glasses that were weathered by atmospheric gases and soil waters over 500 to 3000 years. Good agreement is found between laboratory experiments and slowly leached historical glasses, with a strong enrichment of metals at the water/gel interface. Depending on the nature of the transition elements originally dissolved in the melt, increasing elemental distributions are expected to increase with time for a given glass durability context

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures