Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Clinical Validation of the Ansi C63.19 Draft Standard for Measuring Compatibility between Digital Wireless Phones and Hearing Aids

Acoustic interference can be generated in hearing aids by the pulsed transmission signal of a digital wireless phone. This interference, resembling a buzzing, clicking, or static sound, is annoying and can seriously degrade the intelligibility of the speech. The objective of the ANSI C63.19 Draft Standard is to provide a simple, reliable test procedure for measuring the immunity of hearing aids to this interference. To clinically validate the standard, hearing aids were custom manufactured for eighteen hearing-impaired participants. The participants rated the effects of the interference experienced when using five digital wireless phone technologies (CDMA at 800 and 1900 MHz, TDMA-50 Hz at 800 and 1900 MHz, and TDMA-217 Hz at 1900 MHz) at five transmission power levels (0, 6, 12, 18, and 24 dBm). More than two-thirds of the subjects responded as predicted by acoustic measurements of the interference. The remaining subjects experienced difficulties unrelated to wireless phone interference due to severe hearing loss or excessive feedback. These results support the use of acoustic measurements of immunity as the basis for the ANSI C63.19 standard.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Time-lapse Geophysical Investigations over Known Archaeological Features Using Electrical Resistivity Imaging and Earth Resistance

Electrical methods of geophysical survey are known to produce results that are hard to predict at different times of the year, and under differing weather conditions. This is a problem which can lead to misinterpretation of archaeological features under investigation. The dynamic relationship between a ‘natural’ soil matrix and an archaeological feature is a complex one, which greatly affects the success of the feature’s detection when using active electrical methods of geophysical survey. This study has monitored the gradual variation of measured resistivity over a selection of study areas. By targeting difficult to find, and often ‘missing’ electrical anomalies of known archaeological features, this study has increased the understanding of both the detection and interpretation capabilities of such geophysical surveys. A 16 month time-lapse study over 4 archaeological features has taken place to investigate the aforementioned detection problem across different soils and environments. In addition to the commonly used Twin-Probe earth resistance survey, electrical resistivity imaging (ERI) and quadrature electro-magnetic induction (EMI) were also utilised to explore the problem. Statistical analyses have provided a novel interpretation, which has yielded new insights into how the detection of archaeological features is influenced by the relationship between the target feature and the surrounding ‘natural’ soils. The study has highlighted both the complexity and previous misconceptions around the predictability of the electrical methods. The analysis has confirmed that each site provides an individual and nuanced situation, the variation clearly relating to the composition of the soils (particularly pore size) and the local weather history. The wide range of reasons behind survey success at each specific study site has been revealed. The outcomes have shown that a simplistic model of seasonality is not universally applicable to the electrical detection of archaeological features. This has led to the development of a method for quantifying survey success, enabling a deeper understanding of the unique way in which each site is affected by the interaction of local environmental and geological conditions

A Cytochrome-b Perspective on Passerina Bunting Relationships

We sequenced the complete mitochondrial cytochrome-b gene (1,143 nucleotides) for representatives of each species in the cardinalid genera Passerina (6 species), Guiraca (1 species), and Cyanocompsa (3 species), and used a variety of phylogenetic methods to address relationships within and among genera. We determined that Passerina, as presently recognized, is paraphyletic. Lazuli Bunting (P. amoena) is sister to the much larger Blue Grosbeak (Guiraca caerulea). Indigo Bunting (P. cyanea) and Lazuli Bunting are not sister taxa as generally thought. In all weighted parsimony trees and for the gamma-corrected HKY tree, Indigo Bunting is the sister of two sister groups, a “blue” (Lazuli Bunting and Blue Grosbeak) and a “painted” (Rosita\u27s Bunting [P. rositae], Orange-breasted Bunting [P. leclancherii], Varied Bunting [P. versicolor], and Painted Bunting [P. ciris]) clade. The latter two species form a highly supported sister pair of relatively more recent origin. Uncorrected (p) distances for ingroup (Passerina and Guiraca) taxa range from 3.0% (P. versicolor–P. ciris) to 7.6% (P. cyanea–P. leclancherii) and average 6.5% overall. Assuming a molecular clock, a bunting “radiation” between 4.1 and 7.3 Mya yielded four lineages. This timing is consistent with fossil evidence and coincides with a late-Miocene cooling during which a variety of western grassland habitats evolved. A reduction in size at that time may have allowed buntings to exploit that new food resource (grass seeds). We speculate that the Blue Grosbeak subsequently gained large size and widespread distribution as a result of ecological character displacement

Fundamental Neutron Physics at Spallation Sources

Low-energy neutrons have been a useful probe in fundamental physics studies for more than 70 years. With advances in accelerator technology, many new sources are spallation based. These new, high-flux facilities are becoming the sites for many next-generation fundamental neutron physics experiments. In this review, we present an overview of the sources and the current and upcoming fundamental neutron physics programs

Tragedy Triumph Transformation

A premiere of the Crossroads Project: Emergence.https://digitalcommons.usu.edu/music_programs/1229/thumbnail.jp

The Real and Redshift Space Density Distribution Function for Large-Scale Structure in the Spherical Collapse Approximation

We use the spherical collapse (SC) approximation to derive expressions for the smoothed redshift-space probability distribution function (PDF), as well as the $p$-order hierarchical amplitudes $S_p$, in both real and redshift space. We compare our results with numerical simulations, focusing on the $\Omega=1$ standard CDM model, where redshift distortions are strongest. We find good agreement between the SC predictions and the numerical PDF in real space even for \sigma_L \simgt 1, where $\sigma_L$ is the linearly-evolved rms fluctuation on the smoothing scale. In redshift space, reasonable agreement is possible only for \sigma_L \simlt 0.4. Numerical simulations also yield a simple empirical relation between the real-space PDF and redshift-space PDF: we find that for \sigma \simlt 1, the redshift space PDF, P[\delta_z], is, to a good approximation, a simple rescaling of the real space PDF, P[\delta], i.e., P[\delta/\sigma] d[\delta/\sigma] = P[\delta_z/\sigma_z] d[\delta_z/\sigma_z], where $\sigma$ and \sigma_z are the real-space and redshift-space rms fluctuations, respectively. This result applies well beyond the validity of linear perturbation theory, and it is a good fit for both the standard CDM model and the Lambda-CDM model. It breaks down for SCDM at $\sigma \approx 1$, but provides a good fit to the \Lambda-CDM models for $\sigma$ as large as 0.8.Comment: 9 pages, latex, 12 figures added (26 total), minor changes to conclusions, to appear in MNRA