Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

Suicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim o

Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which perfo

The research aims to study the effect of an increase in funding the Equity by issuing new common shares on revenues ordinary shares, despite the issuance and marketing costs and the introduction of new shareholders that companies incur when issuing new common shares but it is the most important methods used to finance the Equity is funding the common shares it provides money sufficient to finance the large investments of the company and enhance the confidence of dealers with the company, so I designed this research in order to identify the impact of increased funding Equity issue new common shares to common shares revenues.

This research has included some of the theoretical concepts to each of the Equity

Abstract

                 The research dealt with a studying the impact of oil price fluctuations on one of the rules of financial discipline, which is the rule of budget deficit in the Iraqi economy for the period (2003-2020) as it is one of the quarterly economies that rely mainly on volatile oil revenues that fluctuate with oil prices in global markets, and therefore the general budget suffers. from The state of instability and then the government resorts to borrowing for a long time . this deficit in the general budget and increase the debt burden in the public debt.The research aim to measure and study the impact of oil price flu

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

The research aims to investigate the possibility of joint auditing in improving the market value of Iraqi companies listed on the Iraq Stock Exchange. The sample was represented by 10 Iraqi companies listed in the Iraq market for the period from 2014-2017 (2 years before implementation and 2 years after implementation) and the research was based on the idea that joint auditing enhances investor confidence and raises their level of security as a result of providing quality and reliable reports, and thus This indicates good news in the market that reflects on the performance of stocks and the market value of companies that adopt joint auditing. The results of the analysis indicate an improvement in the market value, but this improv

this research paper aims at measuring and analyzing the influence of the stock exchange on the economic development in the Kingdom. This is done through comparing the Gross Domestic Product (GDP) as a changeable factor affiliated with some independent variables in the KSA stock exchange. These variables are (All Share Index {TASI}, Market Value, Trade Volume Index, number of companies and number of shares). The study covers the period from 2003 to 2017 and adapts the measuring method in analyzing this relation using the Multiple Linear Regression (Stepwise) and (SPSS). The study affirmed the validity of the