Sentiment Analysis using Improved Novel Convolutional Neural Network (SNCNN)

Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy

A systematic review of text classification research based on deep learning models in Arabic language

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language

Corpora for sentiment analysis of Arabic text in social media

Different Natural Language Processing (NLP) applications such as text categorization, machine translation, etc., need annotated corpora to check quality and performance. Similarly, sentiment analysis requires annotated corpora to test the performance of classifiers. Manual annotation performed by native speakers is used as a benchmark test to measure how accurate a classifier is. In this paper we summarise currently available Arabic corpora and describe work in progress to build, annotate, and use Arabic corpora consisting of Facebook (FB) posts. The distinctive nature of thesecorpora is that it is based on posts written in Dialectal Arabic (DA) not following specific grammatical or spelling standards. The corpora are annotated with five labels (positive, negative, dual, neutral, and spam). In addition to building the corpus, the paper illustrates how manual tagging can be used to extract opinionated words and phrases to be used in a lexicon-based classifier

Developing resources for sentiment analysis of informal Arabic text in social media

Natural Language Processing (NLP) applications such as text categorization, machine translation, sentiment analysis, etc., need annotated corpora and lexicons to check quality and performance. This paper describes the development of resources for sentiment analysis specifically for Arabic text in social media. A distinctive feature of the corpora and lexicons developed are that they are determined from informal Arabic that does not conform to grammatical or spelling standards. We refer to Arabic social media content of this sort as Dialectal Arabic (DA) - informal Arabic originating from and potentially mixing a range of different individual dialects. The paper describes the process adopted for developing corpora and sentiment lexicons for sentiment analysis within different social media and their resulting characteristics. The addition to providing useful NLP data sets for Dialectal Arabic the work also contributes to understanding the approach to developing corpora and lexicons

Hybrid Technique for Arabic Text Compression

Arabic content on the Internet and other digital media is increasing exponentially, and the number of Arab users of these media has multiplied by more than 20 over the past five years. There is a real need to save allocated space for this content as well as allowing more efficient usage, searching, and retrieving information operations on this content. Using techniques borrowed from other languages or general data compression techniques, ignoring the proper features of Arabic has limited success in terms of compression ratio. In this paper, we present a hybrid technique that uses the linguistic features of Arabic language to improve the compression ratio of Arabic texts. This technique works in phases. In the first phase, the text file is split into four different files using a multilayer model-based approach. In the second phase, each one of these four files is compressed using the Burrows-Wheeler compression algorithm

El método de caso en la enseñanza del pensamiento político árabe contemporáneo: el cierre del periódico pan-árabe al-Ḥayāt como caso de estudio

Backed by the development of postcolonial studies and subaltern studies, the teaching of contemporary Arab political thought as a “border” and interdisciplinary subject must provide students with the theoretical and conceptual tools to respond to the ideological, social, political and intellectual dynamism of contemporary Arab societies in transformation. To apply this methodological approach, this article presents a practical activity based on the case method (CM). As its general objective, the method challenges students to assume learning as a space for cross-cultural reflection through the analysis and argumentation of a real case history: in this instance, the definitive closure of the influential pan-Arab newspaper al-Ḥayāt in March 2020, after almost 75 years of existence. The activity trains students in general and instrumental competences, such as critical text analysis, by positioning them face-to-face with the case under study. After analyzing and evaluating the case elements provided in the classroom, students can apply any previously acquired knowledge about reform, identity, democracy, culture, Arab nationalism, capitalism, etc. Responding to the case in question, students proved to be able to develop alternatives and synthesize their own views. This method also encourages students to analyze their self-perception of this process.Respaldado por el surgimiento de los estudios poscoloniales y los estudios subalternos, la enseñanza del pensamiento político árabe contemporáneo como materia “fronteriza” e interdisciplinaria debe dotar a los estudiantes de las herramientas teóricas y conceptuales para responder al dinamismo ideológico, social, político e intelectual de las sociedades árabes contemporáneas en transformación. Para aplicar este enfoque metodológico, este artículo presenta una actividad práctica basada en la metodología de caso. Su objetivo general es que los alumnos asuman el aprendizaje como un espacio de reflexión intercultural a través del análisis y la argumentación de un caso real: el cierre definitivo en marzo de 2020 del influyente diario panárabe al-Ḥayāt tras casi setenta y cinco años de existencia. La actividad fomenta la formación de competencias generales e instrumentales, como el análisis crítico de textos, a través del posicionamiento de los alumnos frente al caso de estudio. Tras analizar y evaluar sus elementos impartidos en el aula, los estudiantes son capaces de aplicar los conocimientos adquiridos previamente sobre reforma, identidad, democracia, cultura, nacionalismo árabe, capitalismo, etc. Respondiendo al caso en cuestión, se confirma que fueron capaces de desarrollar alternativas y sintetizar sus propios enfoques. Este método también les permitió analizar su autopercepción de este proceso