Intelligent opinion mining and sentiment analysis using artificial neural networks

The article formulates a rigorously developed concept of opinion mining and sentiment analysis using hybrid neural networks. This conceptual method for processing natural-language text enables a variety of analyses of the subjective content of texts. It is a methodology based on hybrid neural networks for detecting subjective content and potential opinions, as well as a method which allows us to classify different opinion type and sentiment score classes. Moreover, a general processing scheme, using neural networks, for sentiment and opinion analysis has been presented. Furthermore, a methodology which allows us to determine sentiment regression has been devised. The paper proposes a method for classification of the text being examined based on the amount of positive, neutral or negative opinion it contains. The research presented here offers the possibility of motivating and inspiring further development of the methods that have been elaborated in this paper.Stuart, KDC.; Majewski, M. (2015). Intelligent opinion mining and sentiment analysis using artificial neural networks. Lecture Notes in Computer Science. 9492:103-110. doi:10.1007/978-3-319-26561-2_13S1031109492Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)Chen, H., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)Majewski, M., Zurada, J.M.: Sentence recognition using artificial neural networks. Knowl. Based Syst. 21(7), 629–635 (2008)Kacalak, W., Stuart, K.D., Majewski, M.: Intelligent natural language processing. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 584–587. Springer, Heidelberg (2006)Kacalak, W., Stuart, K., Majewski, M.: Selected problems of intelligent handwriting recognition. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. Advances in Soft Computing, vol. 41, pp. 298–305. Springer, Cancun (2007)Stuart, K.D., Majewski, M.: Selected problems of knowledge discovery using artificial neural networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part III. LNCS, vol. 4493, pp. 1049–1057. Springer, Heidelberg (2007)Stuart, K., Majewski, M.: A new method for intelligent knowledge discovery. In: Castillo, O., Melin, P., Ross, O.M., Cruz, R.S., Pedrycz, W., Kacprzyk, J. (eds.) IFSA 2007. Advances in Soft Computing, vol. 42, pp. 721–729. Springer, Heidelberg (2007)Stuart, K.D., Majewski, M.: Artificial creativity in linguistics using evolvable fuzzy neural networks. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 437–442. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M.: Evolvable neuro-fuzzy system for artificial creativity in linguistics. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 46–53. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M., Trelis, A.B.: Selected problems of intelligent corpus analysis through probabilistic neural networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part II. LNCS, vol. 6064, pp. 268–275. Springer, Heidelberg (2010)Stuart, K.D., Majewski, M., Trelis, A.B.: Intelligent semantic-based system for corpus analysis through hybrid probabilistic neural networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part I. LNCS, vol. 6675, pp. 83–92. Springer, Heidelberg (2011)Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991

High g-Force Rollercoaster Rides Induce Sinus Tachycardia but No Cardiac Arrhythmias in Healthy Children

This is the final version of the article. Available from Springer Verlag via the DOI in this record.Theme park operators and medical professionals advise children with heart conditions against using rollercoaster rides, but these recommendations are not evidence-based. The underlying assumption is that the combination of adrenergic stimulation through stress and acceleration might trigger arrhythmias in susceptible individuals. We conducted a cross-sectional observational study to assess heart rate and rhythm in healthy children during commercial rollercoaster rides. Twenty healthy children (9 male) aged 11-15 (mean 13.3 ± 1.4) years underwent continuous heart rate and rhythm monitoring (2-lead ECG) from 5 min before until 10 min after each of 4 high speed (>50 km h(-1)), high g-force (>4) commercial rollercoaster rides. Total recording time was 13 h 20 min. No arrhythmic events were detected. Resting heart rate was 81 ± 10 b min(-1) and increased to 158 ± 20 b·min(-1) during rides. The highest mean HR (165 ± 23 b min(-1)) was observed on the ride with the lowest g-force (4.5 g), but one of the highest speeds (100 km h(-1)). Anticipatory tachycardia (126 ± 15 b min(-1)) within 5 min was frequently observed. A 10 min recovery HR (124 ± 17 b min(-1)) was 56 % greater than resting HR. The speed and g-force experienced on roller coasters induce sinus tachycardia but do not elicit pathological arrhythmias in healthy children.This work was supported by the National Institute for Health Research (NIHR) Biomedical Research Unit in Cardiovascular Disease at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol. Dr Pieles is the holder of an NIHR Academic Clinical Lectureship in Paediatric Cardiology. We would like to thank Novacor Ltd (Swanley, Kent, UK) for providing portable ECG monitoring equipment and Thorpe Park (Merlin Entertainments PLC, Poole, Dorset, UK) for their support during data acquisition. We thank all of the children and their families for participating in this study

Cognitive Functioning and Academic Performance in Elementary School Children with Anxious/Depressed and Withdrawn Symptoms

Rationale: Few studies have evaluated the relationship between depressive symptomatology and neuropsychological performance in children without symptomatic depression.Objectives: This study determined the relationship between anxious/depressed and withdrawn symptoms and performance on cognitive and academic achievement measures.Methods: 335 Caucasian and Hispanic children aged 6 to 11 years who participated in the Tucson Children’s Assessment of Sleep Apnea (TuCASA) study were administered a comprehensive neuropsychological battery measuring cognitive functioning and academic achievement. Their parents completed the Child Behavior Checklist (CBCL). Correlations between performance on the cognitive and academic achievement measures and two Internalizing scales from the CBCL were calculated. Comparisons were made between a “Clinical” referral group (using a T-score of > 60 from the CBCL scales) and a “Normal” group, as well as between Caucasians and Hispanics.Results: No differences were found between those participants with increased anxious/depressed or withdrawn symptomson the CBCL and those without increased symptoms with respect to age, gender, ethnicity, or parental education level. However, significant negative correlations were found between these symptoms and general intellectual function, language, visual construction skills, attention, processing speed, executive functioning abilities, aspects of learning and memory, psychomotor speed and coordination, and basic academic skills.Conclusions: These findings support the hypothesis that depressive symptomatology negatively impacts performance on cognitive and academic achievement measures in school-aged children and these findings are not affected by ethnicity. The findings also reinforce the concept that the presence of anxious/depressed or withdrawn symptoms needs to be considered when evaluating poor neuropsychological performance in children

Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data: A case study of reintroduced red kites

Analysis of satellite-telemetry data mostly occurs long after it has been collected, due to the time and effort needed to collate and interpret such material. Such delayed reporting does reduce the usefulness of such data for nature conservation when timely information about animal movements is required. To counter this problem we present a novel approach which combines automated analysis of satellite-telemetry data with rapid communication of insights derived from such data. A relatively simple algorithm (comprising speed of movement and turning angle calculated from fixes), allowed instantaneous detection of excursions away from settlement areas and automated calculation of home ranges on the remaining data Automating the detection of both excursions and home range calculations enabled us to disseminate ecological insights from satellite-tag data instantaneously through a dedicated web portal to inform conservationists and wider audiences. We recommend automated analysis, interpretation and communication of satellite tag and other ecological data to advance nature conservation research and practice

The role of ongoing dendritic oscillations in single-neuron dynamics

The dendritic tree contributes significantly to the elementary computations a neuron performs while converting its synaptic inputs into action potential output. Traditionally, these computations have been characterized as temporally local, near-instantaneous mappings from the current input of the cell to its current output, brought about by somatic summation of dendritic contributions that are generated in spatially localized functional compartments. However, recent evidence about the presence of oscillations in dendrites suggests a qualitatively different mode of operation: the instantaneous phase of such oscillations can depend on a long history of inputs, and under appropriate conditions, even dendritic oscillators that are remote may interact through synchronization. Here, we develop a mathematical framework to analyze the interactions of local dendritic oscillations, and the way these interactions influence single cell computations. Combining weakly coupled oscillator methods with cable theoretic arguments, we derive phase-locking states for multiple oscillating dendritic compartments. We characterize how the phase-locking properties depend on key parameters of the oscillating dendrite: the electrotonic properties of the (active) dendritic segment, and the intrinsic properties of the dendritic oscillators. As a direct consequence, we show how input to the dendrites can modulate phase-locking behavior and hence global dendritic coherence. In turn, dendritic coherence is able to gate the integration and propagation of synaptic signals to the soma, ultimately leading to an effective control of somatic spike generation. Our results suggest that dendritic oscillations enable the dendritic tree to operate on more global temporal and spatial scales than previously thought

Innate Recognition of Fungal Cell Walls

The emergence of fungal infections as major causes of morbidity and mortality in immunosuppressed individuals has prompted studies into how the host recognizes fungal pathogens. Fungi are eukaryotes and as such share many similarities with mammalian cells. The most striking difference, though, is the presence of a cell wall that serves to protect the fungus from environmental stresses, particularly osmotic changes [1]. This task is made challenging because the fungus must remodel itself to allow for cell growth and division, including the conversion to different morphotypes, such as occurs during germination of spherical spores into filamentous hyphae. The cell wall also connects the fungus with its environment by triggering intracellular signaling pathways and mediating adhesion to other cells and extracellular matrices. Here, important facts and concepts critical for understanding innate sensing of the fungal cell wall by mammalian pathogens are reviewed

Methods to identify the target population: implications for prescribing quality indicators

Background: Information on prescribing quality is increasingly used by policy makers, insurance companies and health care providers. For reliable assessment of prescribing quality it is important to correctly identify the patients eligible for recommended treatment. Often either diagnostic codes or clinical measurements are used to identify such patients. We compared these two approaches regarding the outcome of the prescribing quality assessment and their ability to identify treated and undertreated patients. Methods: The approaches were compared using electronic health records for 3214 diabetes patients from 70 general practitioners. We selected three existing prescribing quality indicators (PQI) assessing different aspects of treatment in patients with hypertension or who were overweight. We compared population level prescribing quality scores and proportions of identified patients using definitions of hypertension or being overweight based on diagnostic codes, clinical measurements or both. Results: The prescribing quality score for prescribing any antihypertensive treatment was 93% (95% confidence interval 90-95%) using the diagnostic code-based approach, and 81% (78-83%) using the measurement-based approach. Patients receiving antihypertensive treatment had a better registration of their diagnosis compared to hypertensive patients in whom such treatment was not initiated. Scores on the other two PQI were similar for the different approaches, ranging from 64 to 66%. For all PQI, the clinical measurement -based approach identified higher proportions of both well treated and undertreated patients compared to the diagnostic code -based approach. Conclusions: The use of clinical measurements is recommended when PQI are used to identify undertreated patients. Using diagnostic codes or clinical measurement values has little impact on the outcomes of proportion-based PQI when both numerator and denominator are equally affected. In situations when a diagnosis is better registered for treated than untreated patients, as we observed for hypertension, the diagnostic code-based approach results in overestimation of provided treatment