Sentiment analysis in arabic: opinion polarity detection

Con Mención de Doctorado Internacional[ES]El análisis de sentimientos está obteniendo una gran importancia debido al aumento de popularidad de la web 2.0. Esta memoria se centra en el estudio de diferentes aspectos del análisis de sentimientos. El primer objetivo es analizar las opiniones que provienen del árabe y predecir su polaridad. Para alcanzar este objetivo se han generado dos corpora: OCA y EVOCA. OCA es un corpus de opinión de películas en árabe, y EVOCA es un corpus paralelo a OCA que incluye la traducción al inglés de las opiniones. Otro objetivo consiste en el análisis de sentimientos adaptado a diferentes dominios. Para ello, se ha generado el corpus SINAI-SA y se han aplicado distintas técnicas de aprendizaje automático. Finalmente, en esta memoria se realiza un estudio sobre revisiones neutrales. Para llevar a cabo este objetivo, se han investigado dos enfoque principales, uno basado en orientación semántica y el otro basado en algoritmos de aprendizaje automático como SVM o NB.[EN]Sentiment analysis is becoming increasingly important due the growing popularity of Web 2.0. This study focuses mainly on how to analyze opinions in Arabic language and predict their polarity. To achieve that, two corpora have been generated (OCA and EVOCA), OCA is an opinion corpus for Arabic movie reviews, while EVOCA is the translated version of OCA to English. Another corpus was created (SINAI-SA corpus) used with other corpora in order to predict sentiments in different domains. SINAI corpus was also used to study how to sort comments behave as textual information for the prediction of customer rates. Another question that was solved in this study is “How to treat with the neutral reviews”. Two main approaches have been investigated in this research, one based on semantic orientation and the other one based on machine learning algorithms like SVM or NBTesis Univ. Jaén. Departamento de Informática, leída el 7 de octubre de 201

SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis

Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the difficulties associated with an appropriate data annotation has been underestimated. In this paper, we present a novel approach to automatically construct an annotated sentiment corpus for Algerian dialect (a Maghrebi Arabic dialect). The construction of this corpus is based on an Algerian sentiment lexicon that is also constructed automatically. The presented work deals with the two widely used scripts on Arabic social media: Arabic and Arabizi. The proposed approach automatically constructs a sentiment corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi test sets, respectively. Ongoing work is aimed at integrating transliteration process for Arabizi messages to further improve the obtained results.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Subjectivity and Sentiment Analysis of Arabic: A Survey

Abstract. Subjectivity and sentiment analysis (SSA) has recently gained consid-erable attention, but most of the resources and systems built so far are tailored to English and other Indo-European languages. The need for designing systems for other languages is increasing, especially as blogging and micro-blogging web-sites become popular throughout the world. This paper surveys different tech-niques for SSA for Arabic. After a brief synopsis about Arabic, we describe the main existing techniques and test corpora for Arabic SSA that have been intro-duced in the literature.

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%

A machine-learning approach to negation and speculation detection for sentiment analysis

Recognizing negative and speculative information is highly relevant for sentiment analysis. This paper presents a machine-learning approach to automatically detect this kind of information in the review domain. The resulting system works in two steps: in the first pass, negation/speculation cues are identified, and in the second phase the full scope of these cues is determined. The system is trained and evaluated on the Simon Fraser University Review corpus, which is extensively used in opinion mining. The results show how the proposed method outstrips the baseline by as much as roughly 20% in the negation cue detection and around 13% in the scope recognition, both in terms of F1. In speculation, the performance obtained in the cue prediction phase is close to that obtained by a human rater carrying out the same task. In the scope detection, the results are also promising and represent a substantial improvement on the baseline (up by roughly 10%). A detailed error analysis is also provided. The extrinsic evaluation shows that the correct identification of cues and scopes is vital for the task of sentiment analysis.Maite Taboada from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant 261104- 2008). This work was partly funded by the Spanish Ministry of Education and Science (TIN2009-14057-C03-03 Project) and the Andalusian Ministry of Economy, Innovation and Science (TIC 07629 and TIC 07684 Projects)

Occurrence and sources of natural and anthropogenic lipid tracers in surface soils from arid urban areas of Saudi Arabia

Soil particles contain a variety of natural and anthropogenic organic components, and in urban areas can be considered as local collectors of pollutants. Surface soil samples were taken from ten urban areas in Riyadh during early winter of 2007. They were extracted with dichloromethane-methanol mixture and the extracts were analyzed by gas chromatography-mass spectrometry. The major compounds were unresolved complex mixture (UCM), plasticizers, n-alkanes, carbohydrates, n-alkanoic acids, hopanes, n-alkanols, and sterols. Vegetation detritus was the major natural source of organic compounds (24.0 ± 15.7%) in samples from areas with less human activities and included n-alkanes, n-alkanoic acids, n-alkanols, sterols and carbohydrates. Vehicular emission products and discarded plastics were the major anthropogenic sources in the soil particles (53.3 ± 21.3% and 22.7 ± 10.7%, respectively). The anthropogenic tracers were UCM, plasticizers, n-alkanes, hopanes and traces of steranes. Vegetation and human activities control the occurrence and distribution of natural and anthropogenic extractable organic matter in this arid urban area.Keywords: Petroleum residues, Biomarkers, Soils, Lipids, Plasticizer

FLEXURAL BEHAVIOR OF TWO-LAYER BEAM MADE WITH LIGHT WEIGHT STEEL FIBRE CONCRETE AND RECYCLED AGGREGATE CONCRETE

<h2>Abstract</h2><p>In structural design, it is extremely desirable to use as low-material as possible while keeping integrity and usefulness. Reducing the structure's weight is one strategy for achieving this objective. Steel fibres have recently been added to reinforced concrete beams to increase flexural and shear strength. Fibre reinforcement in structural elements has drawn considerable interest from the building sector. Steel fibre has received the greatest attention and utilization among all fibre types. When compared to plain concrete, incorporating fibres into concrete may result in better crack management and greater strength. This study examines how two-layer beams made of lightweight steel fibre concrete and recycled aggregate concrete flex under bending loads. Twelve distinct beams with cross sections measuring 100 mm, 150 mm, and 1500 mm (width, depth, and length) are prepared and tested as part of the study. These beams are evaluated under four-point bending. In the tension zone of the lightweight concrete layer, different percentages of steel fibre ranging from 0% to 1.5% by volume were introduced. In the concrete compression layer, recycled block aggregate was substituted for natural coarse aggregate in varying percentages (0%, 25%, and 50%). According to the findings, the flexural strength of beams with a higher steel fibre percentage is higher than that of beams with a higher recycled aggregate component. The study also shows that two-layer beams with higher steel fibre content have superior crack management and deflection behavior than those with lower steel content. The results of the flexural reinforced concrete beam test were contrasted with the calculated design strength determined using British Standards.</p&gt