Sentiment analysis of dialectical Arabic social media content using a hybrid linguistic-machine learning approach

Despite the enormous increase in the number of Arabic posts on social networks, the sentiment analysis research into extracting opinions from these posts lags behind that for the English language. This is largely attributed to the challenges in processing the morphologically complex Arabic natural language and the scarcity of Arabic NLP tools and resources. This complex task is further exacerbated when analysing dialectal Arabic that do not abide by the formal grammatical structure. Based on the semantic modelling of the target domain’s knowledge and multi-factor lexicon-based sentiment analysis, the intent of this research is to use a hybrid approach, integrating linguistic and machine learning methods for sentiment analysis classification of dialectal Arabic. First, a dataset of dialectal Arabic tweets was collected focusing on the unemployment domain, which is annotated manually. The tweets cover different dialectal Arabic in Saudi Arabia for which a comprehensive Arabic sentiment lexicon was constructed. This approach to sentiment analysis also integrated a novel light stemming mechanism towards improved Saudi dialectal Arabic stemming. Subsequently, a novel multi-factor lexicon-based sentiment analysis algorithm was developed for domain-specific social media posts written in dialectal Arabic. The algorithm considers several factors (emoji, intensifiers, negations, supplications) to improve the accuracy of the classifications. Applying this model to a central problem of sentiment analysis in dialectical Arabic, these operational techniques were deployed in order to assess analytical performance across social media channels which are vulnerable to semantic and colloquial variations. Finally, this study presented a new hybrid approach to sentiment analysis where domain knowledge is utilised in two methods to combine computational linguistics and machine learning; the first method integrates the problem domain semantic knowledgebase in the machine learning training features set, while the second uses the outcome of the lexicon-based sentiment classification in the training of the machine learning methods. By integrating these techniques into a single, hybridised solution, a greater degree of accuracy and consistency was achieved than applying each approach independently, confirming a pragmatic solution to sentiment classification in dialectical Arabic text

MULDASA:Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialect

Institutional Antecedents of Green Entrepreneurship and Sustainable Development in Saudi Arabia

L'emprenedoria sostenible és un tema que ha despertat un interès creixent en recerca. Portant conjuntament els tres pilars de la sostenibilitat: societat, medi ambient i economia. Els estudiosos consideren que l'emprenedoria sostenible és un camí pràctic i factible cap a aquesta la integració significativa de les persones, els beneficis i el planeta. En el context de l'Aràbia Saudita, aquest estudi analitza la influència de les institucions formals i informals en les activitats emprenedores verdes i el seu impacte en el desenvolupament sostenible. Es va adoptar l'economia institucional per emmarcar la hipòtesis i anàlisi. La metodologia utilitzada va ser quantitativa (regressió i dades de panell models), i les dades es van obtenir dels informes anuals de l'Autoritat de Meteorologia i Protecció del Medi Ambient i Autoritat General d'Estadística (2012-2018). Les principals troballes d'aquest estudi mostren que les institucions (com ara els drets de propietat i la cultura) hi contribueixen positivament emprenedoria verda (vs. no verda), amb una influència positiva en el desenvolupament sostenible, en Aràbia Saudita. Els resultats també demostren que l'emprenedoria verda contribueix positivament a els components econòmics, socials i ambientals del desenvolupament sostenible, mentre que l'emprenedoria no verda no té cap efecte. Aquesta investigació té implicacions tant teòriques com pràctiques. Pel que fa al debat teòric, l'estudi aporta proves empíriques que en destaquen la rellevància d'institucions formals i informals a les activitats empresarials ecològiques i la seva influència desenvolupament sostenible. Així, els responsables polítics que estan creant estratègies constantment poden tenir en compte considerant que qualsevol política implementada afecta l'emprenedoria verda, generant eficaç solucions i oportunitats en infraestructura verda i suport per al seu ús pels governs, empreses privades i totes les parts interessades rellevants.El emprendimiento sostenible es un tema que ha atraído un creciente interés de investigación. trayendo juntos los tres pilares de la sostenibilidad - sociedad, medio ambiente y economía - Los académicos consideran que el emprendimiento sostenible es un camino práctico y factible hacia la integración significativa de las personas, las ganancias y el planeta. En el contexto de Arabia Saudita, este estudio analiza la influencia de las instituciones formales e informales en las actividades empresariales verdes y su impacto en el desarrollo sostenible. Se adoptó la economía institucional para enmarcar la hipótesis y análisis. La metodología utilizada fue cuantitativa (regresión y datos de panel). modelos), y los datos se obtuvieron de los informes anuales de la Autoridad de Meteorología y Protección Ambiental y la Autoridad General de Estadística (2012-2018). Los principales hallazgos de este estudio muestran que las instituciones (como los derechos de propiedad y la cultura) contribuyen positivamente a emprendimiento verde (vs. no verde), con una influencia positiva en el desarrollo sostenible, en Arabia Saudita. Los resultados también demuestran que el emprendimiento verde contribuye positivamente a los componentes económicos, sociales y ambientales del desarrollo sostenible, mientras que el espíritu empresarial no verde no tiene ningún efecto. Esta investigación tiene implicaciones tanto teóricas como prácticas. En cuanto al debate teórico, el estudio aporta evidencia empírica que destaca la relevancia de las instituciones formales e informales a las actividades empresariales verdes y su influencia en desarrollo sostenible. Por lo tanto, los formuladores de políticas que están constantemente creando estrategias pueden tomar en consideración que toda política que se implemente incide en el emprendimiento verde, generando soluciones y oportunidades en infraestructura verde y apoyo para su uso por parte de gobiernos, empresas privadas y todas las partes interesadas pertinentes.Explanatory Summary of the Content of the Thesis Sustainable entrepreneurship is a subject that has attracted growing research interest. Bringing together the three pillars of sustainability - society, the environment, and the economy - sustainable entrepreneurship is considered by scholars to be a practical and feasible path towards the meaningful integration of people, profit, and planet. In the context of Saudi Arabia, this study analyses the influence of formal and informal institutions on green entrepreneurial activities and their impact on sustainable development. Institutional economics was adopted to frame the hypotheses and analysis. The methodology used was quantitative (regression and panel data models), and the data were obtained from the annual reports of the Authority for Meteorology and Environmental Protection and the General Authority for Statistics (2012-2018). The main findings of this study show that institutions (such as property rights and culture) contribute positively to green (vs. non-green) entrepreneurship, with a positive influence on sustainable development, in Saudi Arabia. The results also demonstrate that green entrepreneurship contributes positively to the economic, social, and environmental components of sustainable development, whereas nongreen entrepreneurship has no effect. This research has both theoretical and practical implications. In terms of the theoretical debate, the study provides empirical evidence highlighting the relevance of formal and informal institutions to green entrepreneurial activities and their influence on sustainable development. Thus, policymakers who are constantly creating strategies can take into consideration that any policy implemented affects green entrepreneurship, generating effective solutions and opportunities in green infrastructure and support for their use by governments, private companies, and all the relevant stakeholders

Faktor Yang Mempengaruhi Tingkat Pendapatan dan Kesejahteraan Petani Ikan Ditinjau Dari Jenis Ikan Yang Diusahakan Di Kecamatan Badas Kabupaten Kediri

ABSTRAK   Alwakid, Galih Rasi Milad. 2012. Faktor Yang Mempengaruhi Tingkat Pendapatan dan Kesejahteraan Petani Ikan Ditinjau Dari Jenis Ikan Yang Diusahakan Di Kecamatan Badas Kabupaten Kediri. Skripsi, Jurusan Geografi, Fakultas Ilmu Sosial, Universitas Negeri Malang, Pembimbing: (I) Prof. Dr. H. Ach. Fatchan, M.Pd, M.P, (II) Drs. Marhadi Slamet K, M.Si.   Kata Kunci:tingkat pendapatan, tingkat kesejahteraan, jenis usaha petani ikan Kecamatan Badas merupakan salah satu kecamatan di sebelah utara Ibukota Kabupaten Kediri. Luas wilayahnya mencapai 39,22 Km2 yang terdiri dari 8 desa. Luas pengunaan lahan perikanan di Kecamatan mencapai 45,52 Ha.Masyarakat di  Kecamatan Badas  banyak yang menjadi pengusaha ikan. Petani  ikan  di Kecamatan Badas berjumlah 100 orang, yang terdapat pada dua desa, yakni Desa Krecek dan Desa Canggu.  Masyarakat  memanfaatkan lahan sawah irigasi dan non irigasi untuk pembudidayaan ikan air tawar seperti lele, mujaer, gurame,tawas,tombro, koki  dan nila. Usaha perikanan yang dilakukan penduduk di  kecamatan tersebut berbeda-beda antara petani ikan yang satu dengan yang lainnya. Adanya perbedaan jenis usaha tersebut, memungkinkan perbedaan tingkat pendapatan dan kesejahteraan masyarakatnya. Penelitian ini bertujuan untuk, 1) Mendeskripsikan faktor-faktor yang mempengaruhi tingkat pendapatan dan  kesejahteraan petani ikan berdasarkan dari jenis ikan yang diusahakan di Kecamatan Badas, 2) Mendeskripsikan tingkat pendapatan dan kesejahteraan petani ikan berdasarkan jenis ikan yang diusahakan di Kecamatan Badas, 3) Mendeskripsikan hubungan antara faktor yang mempengaruhi tingkat pendapatan dan kesejahteraan dengan tingkat pendapatan dan kesejahteraan petani ikan.Penelitian ini merupakan penelitian sensus. Penelitian ini menggunakan metode survey dengan sampel semua petani ikan yang terdapat di Desa Canggu dan Desa Krecek. Teknik pengambilan data yang digunakan angket, observasi dan dokumentasi. Teknik analisis data yang digunakan adalah tabulasi tunggal dan tabulasi silang. Hasil penelitian ini menunjukkan faktor yang mempengaruhi tingkat pendapatan dan kesejahteraan petani ikan di Kecamatan Badas meliputi jenis ikan yang dibudidayakan, jenis budidaya, biaya produksi, dan banyaknya produksi ikan. Hasil penelitian juga menunjukkan bahwa tingkat pendapatan dan kesejahteraan petani ikan di Kecamatan Badas mengalami perbedaan antara jenis budidaya yang satu dengan yang lainnya. Berdasarkan hasil analisis diketahui faktor yang mempengaruhi dengan tingkat pendapatan dan kesejahteraan petani ikan adalah jenis budidaya dan jenis ikan yang dibudidayakan. Hasil analisis tabulasi silang menunjukkan bahwa adanya keterkaitan antara jenis budidaya pembesaran ikan hias dengan tingkat pendapatan dan kesejahteraan petani ikan yang paling tinggi, yakni Rp. 6.650.000,00 dengan KS 3. Sedangkan hasil analisis tabulasi silang antara jenis ikan dengan tingkat pendapatan dan kesejahteraan didapatkan jenis usaha ikan dengan jenis ikan hias koki dan komet mempunyai tingkat pendapatan dan kesejahteraan yang paling tinggi, yakni Rp. 9693750,00 dengan KS 3+

Governmental Supportive Policies for Green Entrepreneurial Activity in Saudi Arabia: An Institutional Analysis

In this exploratory study, we analyzed the influence of governmental supportive policies on green entrepreneurial activity in Saudi Arabia, using institutional economics as a theoretical framework. Based on data from reports of the General Authority for Meteorology and Environmental Protection and the General Authority for Statistics, both fixed and random effects models were applied in 21 cities within Saudi Arabia (2014–2018). The main findings show that environmental, innovation, and entrepreneurship governmental supportive policies exert a significantly positive influence on green entrepreneurship. Future implications for policymaking and managerial decisions related to targeted awareness raising and environmental care can be derived from this study

Cultural antecedents of green entrepreneurship in Saudi Arabia: an institutional approach

Recent decades have brought cultural changes toward the increase of environmentally-friendly initiatives such as green entrepreneurship. Some countries are failing to develop environmental initiatives, whereas others are transitioning and advancing toward this new trend. In particular, Saudi Arabia has initiated efforts toward becoming an ecologically-friendly society. Motivated by this, we explore whether cultural characteristics are associated with green entrepreneurship in Saudi Arabia. Institutional economics is adopted to frame our hypotheses and analysis. The hypothesized relationships were empirically tested in a sample of 84 observations from 21 cities during the period 2015–2018. Data were collected from reports by the Saudi General Authority and analyzed through regression models. The main results show that cultural characteristics, such as environmental actions, environmental consciousness, and temporal orientation, increase the level of green entrepreneurial activity across cities in Saudi Arabia. The findings of this study contribute to existing knowledge on green entrepreneurship, as well as to the discussion of implications for policy and practice related to environmentally-friendly productive activities

The influence of green entrepreneurship on sustainable development in Saudi Arabia: The role of formal institutions

This study explores the influence of green entrepreneurial activity on sustainable development, using institutional economics as a theoretical framework. Also, the role of entrepreneurship policy is analysed in the context of Saudi Arabia. Using information from the General Authority for Statistics from 13 Saudi Arabian cities, the main findings show that green entrepreneurship positively contributes to the economic, social, and environmental components of sustainable development during the period 2012–2017. These results demonstrate a measurable indication of sustainable development outcomes, whereby Saudi Arabian institutions align entrepreneurial activities with a positive triple bottom line effect. Accordingly, these findings contribute new evidence to justify government commitment to supporting green entrepreneurship in Saudi Arabia and encourage future domestic policies

Unsupervised Outlier Detection in IOT Using Deep VAE

The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT’s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data’s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%

Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency

Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes