Using Word Embeddings in Twitter Election Classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to train and generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance. By comparing the classification results of two word embedding models, which are trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data type should align with the Twitter classification dataset to achieve a better performance. Moreover, by evaluating the results of word embeddings models trained using various context window sizes and dimensionalities, we found that large context window and dimension sizes are preferable to improve the performance. Our experimental results also show that using word embeddings and CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings

Enhancing design learning using groupware

Project work is increasingly used to help engineering students integrate, apply and expand on knowledge gained from theoretical classes in their curriculum and expose students to 'real world' tasks [1]. To help facilitate this process, the department of Design, Manufacture and Engineering Management at the University of Strathclyde has developed a web±based groupware product called LauLima to help students store, share, structure and apply information when they are working in design teams. This paper describes a distributed design project class in which LauLima has been deployed in accordance with a Design Knowledge Framework that describes how design knowledge is generated and acquired in industry, suggesting modes of design teaching and learning. Alterations to the presentation, delivery and format of the class are discussed, and primarily relate to embedding a more rigorous form of project-based learning. The key educational changes introduced to the project were: the linking of information concepts to support the design process; a multidisciplinary team approach to coaching; and a distinction between formal and informal resource collections. The result was a marked improvement in student learning and ideation

Software Process Modeling with Eclipse Process Framework

The software development industry is constantly evolving. The rise of the agile methodologies in the late 1990s, and new development tools and technologies require growing attention for everybody working within this industry. The organizations have, however, had a mixture of various processes and different process languages since a standard software development process language has not been available. A promising process meta-model called Software & Systems Process Engineering Meta- Model (SPEM) 2.0 has been released recently. This is applied by tools such as Eclipse Process Framework Composer, which is designed for implementing and maintaining processes and method content. Its aim is to support a broad variety of project types and development styles. This thesis presents the concepts of software processes, models, traditional and agile approaches, method engineering, and software process improvement. Some of the most well-known methodologies (RUP, OpenUP, OpenMethod, XP and Scrum) are also introduced with a comparison provided between them. The main focus is on the Eclipse Process Framework and SPEM 2.0, their capabilities, usage and modeling. As a proof of concept, I present a case study of modeling OpenMethod with EPF Composer and SPEM 2.0. The results show that the new meta-model and tool have made it possible to easily manage method content, publish versions with customized content, and connect project tools (such as MS Project) with the process content. The software process modeling also acts as a process improvement activity.Ohjelmistoprosessin mallinnus Eclipse Process Frameworkilla ja SPEM 2.0 metamallilla Ohjelmistot ja ohjelmistoteollisuus kehittyvät jatkuvasti. Ketterien menetelmien tulo 1990-luvun loppupuolella, uudet kehitystyökalut ja teknologiat vaativat yhä enemmän huomiota alalla työskenteleviltä ihmisiltä. Organisaatioilla on kuitenkin ollut sekalainen kirjo prosesseja ja erilaisia prosessikuvauskieliä, koska standardia kuvauskieltä ei ole ollut saatavilla. Prosessimetamalli SPEM 2.0 julkaistiin hiljattain. Tätä mallia hyödyntää mm. Eclipse Process Framework Composer (EPFC) –työkalu, joka on suunniteltu prosessien ja menetelmäsisällön kehittämiseen ja ylläpitoon. Työkalun tavoitteena on tukea useita erilaisia projektityyppejä ja kehitystyylejä. Tässä työssä esitellään seuraavat aiheet ja käsitteet: ohjelmistoprosessit, mallit, perinteiset ja ketterät lähestymistavat, metoditekniikkaa sekä prosessien kehittäminen. Lisäksi tutustutaan muutamiin tunnetuimmista metodologioista (RUP, OpenUP, OpenMethod, XP ja Scrum) ja vertaillaan näitä. Työssä tutkitaan tarkemmin Eclipse Process Framework Composer –työkalua, SPEM 2.0 metamallia, näiden ominaisuuksia, käyttöä sekä mallintamista. Esitän tutkimustulokset ja tutkimuksenkulun OpenMethodin mallintamisesta EPFC –työkalulla sekä SPEM 2.0 -metamallilla. Tulokset osoittavat, että uusi metamalli ja työkalu helpottavat prosessin ja menetelmäsisällön hallintaa, mahdollistavat räätälöityjen julkaisujen teon sisällöstä, sekä yhdistävät prosessin projektityökaluihin kuten MS Projectiin. Mallinnus voidaan lisäksi ymmärtää osana prosessin kehittämistä.Siirretty Doriast

Uncovering collective listening habits and music genres in bipartite networks

In this paper, we analyze web-downloaded data on people sharing their music library, that we use as their individual musical signatures (IMS). The system is represented by a bipartite network, nodes being the music groups and the listeners. Music groups audience size behaves like a power law, but the individual music library size is an exponential with deviations at small values. In order to extract structures from the network, we focus on correlation matrices, that we filter by removing the least correlated links. This percolation idea-based method reveals the emergence of social communities and music genres, that are visualised by a branching representation. Evidence of collective listening habits that do not fit the neat usual genres defined by the music industry indicates an alternative way of classifying listeners/music groups. The structure of the network is also studied by a more refined method, based upon a random walk exploration of its properties. Finally, a personal identification - community imitation model (PICI) for growing bipartite networks is outlined, following Potts ingredients. Simulation results do reproduce quite well the empirical data.Comment: submitted to PR