Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

Domain-specific application analysis for customized instruction identification

With the increasing importance of Application Domain Specific Processor (ADSP) design, a significant challenge is to identify special-purpose operations for implementation as a customized instruction. While many methodologies have been proposed for this purpose, they all work for a single algorithm chosen from the target application domain. Such algorithm-specific approaches are not suitable for designing instruction sets applicable to a whole family of related algorithms. For an entire range of related algorithms, this paper develops a methodology for identifying compound operations, as a basis for designing “domain-specific” Instruction Set Architectures (ISAs) that can efficiently run most of the algorithms in a given domain. Our methodology combines three different static analysis techniques to identify instruction sequences common to several related algorithms: identification of (non-branching) instruction sequences that occur commonly across the algorithms; identification of instruction sequences nested within iterative constructs that are thus executed frequently; and identification of commonly-occurring instruction sequences that span basic blocks. Choosing different combinations of these results enables us to design domain-specific special operations with different desired characteristics, such as performance or suitability as a library function. To demonstrate our approach, case studies are carried out for a family of thirteen string matching algorithms. Finally, the validity of our static analysis results is confirmed through independent dynamic analysis experiments and performance improvement measurements.

Parallel parsing of programming languages

A new method of specifying the syntax of programming languages, known as hierarchical language specifications (HLS), is proposed. Efficient parallel algorithms for parsing languages generated by HLS are presented. These algorithms run on an exclusive-read exclusive-write parallel random-access machine. They require O(n) processors and O(log2n) time, where n is the length of the string to be parsed. The most important feature of these algorithms is that they do not use a stack.

PROMIDS: A PROtotype multi-rIng data flow system for functional programming languages

This paper presents a detailed description of the hardware design and implementation of PROMIDS: a PROtotype Multi-rIng Data flow System for functional programming languages. The hardware constraints and the design trade-offs are discussed. The design of the functional units is described in detail. Finally, we report our experience with PROMIDS.

Unified form language: A domain-specific language for weak formulations of partial differential equations

We present the Unified Form Language (UFL), which is a domain-specific language for representing weak formulations of partial differential equations with a view to numerical approximation. Features of UFL include support for variational forms and functionals, automatic differentiation of forms and expressions, arbitrary function space hierarchies formultifield problems, general differential operators and flexible tensor algebra. With these features, UFL has been used to effortlessly express finite element methods for complex systems of partial differential equations in near-mathematical notation, resulting in compact, intuitive and readable programs. We present in this work the language and its construction. An implementation of UFL is freely available as an open-source software library. The library generates abstract syntax tree representations of variational problems, which are used by other software libraries to generate concrete low-level implementations. Some application examples are presented and libraries that support UFL are highlighted. © 2014 ACM.

A dichotomic search algorithm for mining and learning in domain-specific logics

Ferr?, S. and King, R. D. (2004) A dichotomic search algorithm for mining and learning in domain-specific logics. Fundamenta Informaticae. IOS Press. To appear

Modelling an e-learning environment for learning programming languages

It is widely accepted that solving programming exercises is fundamental to learn how to program. Nevertheless, solving exercises is only effective if students receive an assessment on their work. An exercise solved wrong will consolidate a false belief, and without feedback many students will not be able to overcome their difficulties. However, creating, managing and accessing a large number of exercises, covering all the points in the curricula of a programming course, in classes with large number of students, can be a daunting task without the appropriated tools working in unison. This involves a diversity of tools, from the environments where programs are coded, to automatic program evaluators providing feedback on the attempts of students, passing through the authoring, management and sequencing of programming exercises as learning objects. We believe that the integration of these tools will have a great impact in acquiring programming skills. Our research objective is to manage and coordinate a network of eLearning systems where students can solve computer programming exercises. Networks of this kind include systems such as learning management systems (LMS), evaluation engines (EE), learning objects repositories (LOR) and exercise resolution environments (ERE). Our strategy to achieve the interoperability among these tools is based on a shared definition of programming exercise as a Learning Object (LO).

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

Heterogeneity of photosystem II as it occurs in domain specific regions of the thylakoid membrane of spinach (spinacia oleracia L.)

Thylakoid membrane fractions were prepared from specific regions of thylakoid membranes of spinach (Spinacia oleracea). These fractions, which include grana (83), stroma (T3), grana core (8S), margins (Ma) and purified stroma (Y100) were prepared using a non-detergent method including a mild sonication and aqueous two-phase partitioning. The significance of PSlla and PSII~ centres have been described extensively in the literature. Previous work has characterized two types of PSII centres which are proposed to exist in different regions of the thylakoid membrane. a-centres are suggested to aggregate in stacked regions of grana whereas ~-centres are located in unstacked regions of stroma lamellae. The goal of this study is to characterize photosystem II from the isolated membrane vesicles representing different regions of the higher plant thylakoid membrane. The low temperature absorption spectra have been deconvoluted via Gaussian decomposition to estimate the relative sub-components that contribute to each fractions signature absorption spectrum. The relative sizes of the functional PSII antenna and the fluorescence induction kinetics were measured and used to determine the relative contributions of PSlla and PSII~ to each fraction. Picosecond chlorophyll fluorescence decay kinetics were collected for each fraction to characterize and gain insight into excitation energy transfer and primary electron transport in PSlla and PSII~ centres. The results presented here clearly illustrate the widely held notions of PSII/PS·I and PSlIa/PSII~ spatial separation. This study suggests that chlorophyll fluorescence decay lifetimes of PSII~ centres are shorter than those of PSlIa centres and, at FM, the longer lived of the two PSII components renders a larger yield in PSlIa-rich fractions, but smaller in PSIlr3-rich fractions.

A Generic Software Architecture for a Domain Specific Distributed Embedded System

In this paper, we have evolved a generic software architecture for a domain specific distributed embedded system. The system under consideration belongs to the Command, Control and Communication systems domain. The systems in such domain have very long operational lifetime. The quality attributes of these systems are equally important as the functional requirements. The main guiding principle followed in this paper for evolving the software architecture has been functional independence of the modules. The quality attributes considered most important for the system are maintainability and modifiability. Architectural styles best suited for the functionally independent modules are proposed with focus on these quality attributes. The software architecture for the system is envisioned as a collection of architecture styles of the functionally independent modules identified