Sentiment Analysis for Modern Standard Arabic and Colloquial

Arabic language has very rich vocabulary. It is manifested in different forms. The formal, Modern Standard Arabic (MSA), and the informal, colloquial or dialects. Dialectical languages become important as a result of the proliferation of social networks which resulted in the vast unstructured dialectical text available on the web. Unique properties of modern standard Arabic and dialects present major challenges to build sentiment analysis systems by adopting similar models designed for the English language. In this paper, we present a supervised Arabic sentiment analysis using a bag-of-words feature. We further examine using a set of key words (lexicon) for better polarity classification. The testing of the system is carried out on the freely-available Arabic books' reviews (LABR) dataset. LABR includes both modern standard Arabic and Egyptian dialectal reviews. We used both balanced and unbalanced datasets. Clearly, the balanced data set is small in size and, henceforth, a large-scale balanced dataset is required for training of the classifier model. Further, we compared the computed predicted sentiments against the actual reviews for a specific book. Findings, by annotators, had indicated ambiguity between a review and its rating when verified alongside the predicted sentiment, which provided a more reasonable result. Moreover, working with dialects and sarcasm is exceedingly exciting. Experimental results on the adopted logistic classifier model and LABR are encouraging and promising. However, a key prerequisite is the availability of rich and well represented datasets in order to develop robust and efficient Arabic sentiment analyzers.

Kuwaiti Arabic (KA), like other Arabic dialects, is a spoken variety of Arabic that does not have a standardized written convention contrary to Modern Standard Arabic (MSA). With the emergence and spread of social media platforms, Arabic dialects have found their way into the written medium, and hence a need arose to process them alongside MSA. The biggest challenge facing NLP tools is that dialects do not have consistent written conventions contrary to MSA, and writers expressing their dialects usually follow a phonetic writing system, or they write words as they pronounce them. This has opened the door for variations within the same dialect and between dialects and MSA. Furthermore, a prerequisite for analysing any language or dialect is the presence of clear written conventions. Therefore, efforts have been made to establish written conventions for Arabic dialects, but the Kuwaiti dialect has not received the required attention. The current study offers a practical solution for processing written KA. It identified and extracted the written conventions of KA from natural data collected from over 100K Kuwaiti tweets since they represent a good model of natural language. The morphological analyzer (MADAMIRA) - which is designed to process MSA - was enhanced with the extracted criteria. Furthermore, the study involved enriching the analyzer with a dictionary of Kuwaiti terms and vocabulary ‘lemmas’ collected from the Encyclopaedia of Kuwaiti Arabic and from the most used Kuwaiti words on Twitter (currently X). Providing the analyzer with this dictionary of KA words helps it process KA more accurately. The expanded version of the analyzer (MADAMIRA-KA) is the first of its kind designed entirely to process the Kuwaiti dialect and has achieved excellent performance in analyzing over 100K Kuwaiti tweets successfully. The importance of this study lies in developing such a morphological analyzer, which can be used for automated translation, dialect recognition and sentiment analysis.

Sentiment Analysis (SA) is focused on mining opinion (identification and classification) from unstructured text data such as product reviews or microblogs. It is widely used for brand reviews, political campaigns, marketing analysis or gaining feedback from customers. One of the prominent approaches for SA is using supervised machine learning (SML), an algorithm that uses datasets with defined class labels based on mathematical learning from a training dataset. While the results are promising especially with in-domain sentiments, there is no guarantee the model provides the same performance against real time data due to the diversity of new data. In addition, previous studies suggest the result of SML decrease when applied to cross-domain datasets because new features are appeared in different domains. So far, studies in SA emphasise the improvement of the sentiment result whereas there is little discussion focusing on how to detect the degradation of performance for the proposed model. Therefore, we provide a method known as Contextual Analysis (CA), a mechanism that constructs a relationship between words and sources that is constructed in a tree structure identified as Hierarchical Knowledge Tree (HKT). Then, Tree Similarity Index (TSI) and Tree Differences Index (TDI), a formula generate from tree structure are proposed to find similarity as well as changes between train and actual dataset. The regression analysis of datasets reveals that there is a highly significant positive relationship between TSI and SML accuracies. As a result, the prediction model created indicated estimation error within 2.75 to 3.94 and 2.30 for 3.51 for average absolute differences. Moreover, this method also can cluster sentiment words into positive and negative without having any linguistics resources used and at the same time capturing changes of sentiment words when a new dataset is applied.

The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, however, not one which a computer can perform by default. Identifying sentiments in an automated, algorithmic manner will be a useful capability for business and research in their search to understand what consumers think about their products or services and to understand human sociology. Here we propose two new Genetic Algorithms (GAs) for the task of automated text sentiment analysis. The GAs learn whether words occurring in a text corpus are either sentiment or amplifier words, and their corresponding magnitude. Sentiment words, such as 'horrible', add linearly to the final sentiment. Amplifier words in contrast, which are typically adjectives/adverbs like 'very', multiply the sentiment of the following word. This increases, decreases or negates the sentiment of the following word. The sentiment of the full text is then the sum of these terms. This approach grows both a sentiment and amplifier dictionary which can be reused for other purposes and fed into other machine learning algorithms. We report the results of multiple experiments conducted on large Amazon data sets. The results reveal that our proposed approach was able to outperform several public and/or commercial sentiment analysis algorithms.

The existing methods for Chinese sentiment Labeling mainly relies on the artificial sentiment corpus, but a sentiment word in the corpus may not be sentiment words in different sentences. This paper proposes a new method to label the words in the sentences by combining deep convolution neural network with sequential algorithm., We first extract the aspects comprised by words vectors, part of speech vectors, dependent syntax vectors to train the deep convolution neural network, and then employ the sequential algorithm to obtain the sentiment annotation of the sentence. Experimental results verify that our method is effective for sentiment labeling. Considering that the identification of the implicit aspects can improve the completeness of sentiment analysis, we suggest to construct the tuples including aspect, sentiment shifter, sentiment intensity, sentiment words after obtaining the sentiment labels for each word in the sentence. We develop new algorithm for implicit aspect identification by taking the two key factors of the aspects as a topic and the match degree of aspects and sentiment words, and the human language habit. The experiment demonstrates that the algorithm can effectively identify the implicit aspect. In this paper, we solve the problem of sentiment labeling and implicit aspect recognition in sentiment analysis. As a new tool for sentiment analysis, our method can be applied to the enterprise management information analysis, such as product online review, product online reputation, brand image and consumer preference management, and can also be used for the sentiment analysis of large-scale text data.

As the social applications are gaining the more popularity, different kinds of social media platform are ready to publish and express the emotions in the form of reviews. In which, detecting the concealed sentiment patterns and discovering knowledge in the huge user-generated inputs is a challenging task and has greater social significance value. In traditional sentiment analysis approaches, statistical correlation between words is considered. Whereas, dependency factor between aspects and the sentiment words are ignored, which has a greater impact factor on overall sentiment analysis. In this paper, we propose a new supervised topic level sentiment model (SSM), which is capable of handling overall sentiment analysis problems. Belief maximisation algorithm is used in SSM model, and Dirichlet distribution is used to estimate aspects and sentiment words. In order to prepare and modernise new documents, a hyperparameter Gibbs sampling method is used. We conducted experiments on reviews related to different products in multiple documents and the results state that the SSM model outperforms the on-hand algorithm in terms of aspect recognition and overall sentiment prediction accuracy.

As the social applications are gaining the more popularity, different kinds of social media platform are ready to publish and express the emotions in the form of reviews. In which, detecting the concealed sentiment patterns and discovering knowledge in the huge user-generated inputs is a challenging task and has greater social significance value. In traditional sentiment analysis approaches, statistical correlation between words is considered. Whereas, dependency factor between aspects and the sentiment words are ignored, which has a greater impact factor on overall sentiment analysis. In this paper, we propose a new supervised topic level sentiment model (SSM), which is capable of handling overall sentiment analysis problems. Belief maximisation algorithm is used in SSM model, and Dirichlet distribution is used to estimate aspects and sentiment words. In order to prepare and modernise new documents, a hyperparameter Gibbs sampling method is used. We conducted experiments on reviews related to different products in multiple documents and the results state that the SSM model outperforms the on-hand algorithm in terms of aspect recognition and overall sentiment prediction accuracy.

As digital marketing is becoming more popular, the number of customer views on brands is increasing rapidly. This makes it harder for companies to assess their brand image or digitally market their products on the web. We present a lexicon-based approach to extracting sentiment and emotion from tweets for digital marketing purposes. We collect ten thousand tweets related to ten technology brands: Apple, Google, Microsoft, Samsung, GE, IBM, Intel, Facebook, Oracle and HP. We perform sentiment analysis using SentiWordNet while we detect emotions using the NRC Hashtag Emotion Lexicon. We compare and combine the scores obtained from the two lexicons into one result per tweet. We describe the execution process of our experiment and show that the accuracy of the combined approach of sentiment and emotion analysis is enhanced over the independent approaches of sentiment analysis or emotion analysis.

Arabic language suffers from the lack of available large datasets for machine learning and sentiment analysis applications. This work adds to the recently reported large dataset BRAD, which is the largest Book Reviews in Arabic Dataset. In this paper, we introduce HARD (Hotel Arabic-Reviews Dataset), the largest Book Reviews in Arabic Dataset for subjective sentiment analysis and machine language applications. HARD comprises of 490587 hotel reviews collected from the Booking.com website. Each record contains the review text in the Arabic language, the reviewer’s rating on a scale of 1 to 10 stars, and other attributes about the hotel/reviewer. We make available the full unbalanced dataset as well as a balanced subset. To examine the datasets, we implement six popular classifiers using Modern Standard Arabic (MSA) as well as Dialectal Arabic (DA). We test the sentiment analyzers for polarity and rating classifications. Furthermore, we implement a polarity lexicon-based sentiment analyzer. The findings confirm the effectiveness of the classifiers and the datasets. Our core contribution is to make this benchmark-dataset available and accessible to the research community on Arabic language.

<span lang="EN-US">Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.</span>

Understanding and comprehending humans' views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e‐commerce platforms has led to an abundance of the real‐time and free forms of opinions floating on social media platforms. This real‐world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text‐based SA, audio‐based SA, and fusion of text‐audio features‐based SA. This article discusses the various novel ways of classifying fuzzy logic‐based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance. Finally, the article outlines a taxonomy for sentiment classification based on the technique‐supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy‐based SA approaches into five different classes vis‐a‐vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy‐rule based SA, (d) Neuro‐fuzzy network‐based SA, and (e) Fuzzy Emotion Recognition.This article is categorized under:Algorithmic Development > Text MiningFundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

With the accelerated evolution of social networks, there is a tremendous increase in opinions by the people about products or services. While this user-generated content in natural language is intended to be valuable, its large amounts require use of content mining methods and NLP to uncover the knowledge for various tasks. In this study, sentiment analysis is used to analyze and understand the opinions of users using statistical approaches, knowledge-based approaches, hybrid approaches, and concept-based ontologies. Unfortunately, sentiment analysis also experiences a range of difficulties like colloquial words, negation handling, ambiguity in word sense, coreference resolution, which highlight another perspective emphasizing that sentiment analysis is certainly a restricted NLP problem. The purpose of this chapter is to discover how sentiment analysis is a restricted NLP problem. Thus, this chapter discussed the concept of sentiment analysis in the field of NLP and explored that sentiment analysis is a restricted NLP problem due to the sophisticated nature of natural language.

In natural language processing (NLP), Sentiment analysis is flourishing rapidly as it aims at finding the emotional tone behind a piece of text and helps in understanding the attitudes, opinions, and emotions expressed within an online mention. The internet is flooded with large amounts of data such as texts, photos, audio, and video. With the help of sentiment analysis, we can find useful information from this data. This paper presents various approaches in sentiment analysis used by researchers and the major challenges faced in this field. By analyzing different papers, we came to know that the most widely used techniques in machine learning are Naïve Bayes and SVM and in NLP, the lexicon-based approach is used. With the advancement in technology, sentiment analysis will be available for the public and smaller companies as well.

Named entities recognition (NER) has become, over time, a potentially helpful pre-processing for several NLP tasks. It mainly identifies and classifies entities in the text into predefined categories, such as a person, location, and organization. Most of the work done in Arabic named entity recognition (ANER) has focused on Modern Standard Arabic (MSA). However, most scripts on the internet, especially in social media (which are currently a source for corpus development), are in a dialectal form that does not follow standard writing rules. This paper investigates the possibility of deep learning based on named entity recognition in Algerian dialect script through a comparative study between 5 models: AraBERT, DziriBERT, MARBERT, ARBERT, and mBERT. We chose these five models for two significant reasons; the first one, that these models are already pre-trained on the MSA and Arabic dialect text, the second reason, that they have proved their efficiency for other tasks such as Part of Speech Tagging, sentiment analysis, and ANER.KeywordsNLPDeep learningAlgerian dialectNamed Entity RecognitionSocial media

Sentiment analysis has emerged as a powerful tool in financial markets, offering the ability to harness market participants' collective wisdom and emotions to make more informed investment decisions. This research paper presents a comprehensive literature review on sentiment analysis models in predicting stock prices, focusing on the Bank Nifty Index, a critical benchmark in the Indian financial sector. The review begins by elucidating the significance of sentiment analysis in stock price prediction, highlighting its pivotal role in capturing market sentiment, investor emotions, and their impact on asset valuations. A detailed overview of sentiment sources, encompassing financial news, social media, reports, and other relevant data streams, provides insight into the rich tapestry of information that sentiment analysis can leverage. The paper delves into various sentiment analysis approaches, from traditional lexicon-based and rule-based methods to cutting-edge machine learning and deep learning techniques. It elucidates the intricate process of integrating sentiment analysis with stock price prediction models, exploring feature concatenation, time series integration, and weighted data to incorporate sentiment insights effectively. Evaluation metrics crucial for assessing the performance of both sentiment analysis models and stock price prediction models are thoroughly discussed. It concludes by highlighting the substantial potential of sentiment analysis in augmenting stock price prediction, offering investors and traders valuable tools to navigate the complexities of financial markets.