A Hybrid Approach to Sentiment Analysis with Benchmarking Results

The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Naïve Bayes and Maximum Entropy. Using the well known and publicly available Movie Review Dataset, the proposed hybrid system achieved higher accuracy and precision than Naïve Bayes (NB) and Maximum Entropy (ME)

An Evaluation Framework and Adaptive Architecture for Automated Sentiment Detection

Analysts are often interested in how sentiment towards an organization, a product or a particular technology changes over time. Popular methods that process unstructured textual material to automatically detect sentiment based on tagged dictionaries are not capable of fulfilling this task, even when coupled with part-of-speech tagging, a standard component of most text processing toolkits that distinguishes grammatical categories such as article, noun, verb, and adverb. Small corpus size, ambiguity and subtle incremental change of tonal expressions between different versions of a document complicate sentiment detection. Parsing grammatical structures, by contrast, outperforms dictionary-based approaches in terms of reliability, but usually suffers from poor scalability due to its computational complexity. This work provides an overview of different dictionary- and machine-learning-based sentiment detection methods and evaluates them on several Web corpora. After identifying the shortcomings of these methods, the paper proposes an approach based on automatically building Tagged Linguistic Unit (TLU) databases to overcome the restrictions of dictionaries with a limited set of tagged tokens

A linguistic approach to assess the dynamics of design team preference in concept selection

This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an individual’s evaluation of an alternative through natural language based on the Systemic-Functional Linguistics system of APPRAISAL. The linguistic model accounts for both the degree of intensity and the uncertainty of expressed evaluations. Second, this multi-dimensional linguistic model is converted into a scalar to represent the degree of intensity and a probability distribution function for the stated evaluation. Finally, we present a Markovian model to calculate the time-varying change in preferential probability, the probability that an alternative is the most preferred alternative. We further demonstrate how preferential probability toward attributes of alternatives correspond to preferential probability toward alternatives. We illustrate the method on two case studies to highlight the time-variant dynamics of preferences toward alternatives and attributes. This research contributes to process tracing in descriptive decision science to understand how engineers actually take decisions.National Science Foundation (U.S.) (Award CMMI-0900255

Experimental investigation and numerical modelling of the cyclic plasticity and fatigue behavior of additively manufactured 316 L stainless steel

This study addresses the critical need for a constitutive model to analyze the cyclic plasticity of additively manufactured 316L stainless steel. The anisotropic behavior at both room temperature and 300 degrees C is investigated experimentally based on cyclic hysteresis loops performed in different orientations with respect to the build direction. A comprehensive constitutive model is proposed, that integrates the Armstrong-Frederick nonlinear kinematic hardening, Voce nonlinear isotropic hardening and Hill's anisotropic yield criterion within a 3D return mapping algorithm. The model was calibrated to specimens in the 0 degrees and 90 degrees orientations and validated with specimens in the 45 degrees orientation. A single set of hardening parameters successfully represented the elastoplastic response for all orientations at room temperature. The algorithm effectively captured the full cyclic hysteresis loops, including historical effects observed in experimental tests. A consistent trend of reduced hardening was observed at elevated temperature, while the 45 degrees specimen orientation consistently exhibited the highest degree of strain hardening. The applicability of the model was demonstrated by computing energy dissipation for stabilized hysteresis loops, which was combined with fatigue tests to propose an energy-based fatigue life prediction model

Post-transcriptional control of executioner caspases by RNA-binding proteins

Caspases are key components of apoptotic pathways. Regulation of caspases occurs at several levels, including transcription, proteolytic processing, inhibition of enzymatic function, and protein degradation. In contrast, little is known about the extent of post-transcriptional control of caspases. Here, we describe four conserved RNA-binding proteins (RBPs)—PUF-8, MEX-3, GLD-1, and CGH-1—that sequentially repress the CED-3 caspase in distinct regions of the Caenorhabditis elegans germline. We demonstrate that GLD-1 represses ced-3 mRNA translation via two binding sites in its 3′ untranslated region (UTR), thereby ensuring a dual control of unwanted cell death: at the level of p53/CEP-1 and at the executioner caspase level. Moreover, we identified seven RBPs that regulate human caspase-3 expression and/or activation, including human PUF-8, GLD-1, and CGH-1 homologs PUM1, QKI, and DDX6. Given the presence of unusually long executioner caspase 3′ UTRs in many metazoans, translational control of executioner caspases by RBPs might be a strategy used widely across the animal kingdom to control apoptosis

Annotating expressions of Appraisal in English

In the context of Systemic Functional Linguistics, Appraisal is a theory describing the types of language utilised in communicating emotion and opinion. Robust automatic analyses of Appraisal could contribute in a number of ways to computational sentiment analysis by: distinguishing various types of evaluation, for example affect, ethics or aesthetics; discriminating between an author's opinions and the opinions of authors referenced by the author and determining the strength of evaluations. This paper reviews the typology described by Appraisal, presents a methodology for annotating Appraisal, and the use of this to annotate a corpus of book reviews. It discusses an inter-annotator agreement study, and considers instances of systematic disagreement that indicate areas in which Appraisal may be refined or clarified. Although the annotation task is difficult, there are many instances where the annotators agree; these are used to create a gold-standard corpus for future experimentation with Appraisal