Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications

Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts.

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.

Purpose This study aims to develop a conceptual model of positive employee experience using sentiment analysis within algorithm-based human resource (HR) strategies. Its goal is to enhance HR professionals’ understanding of employee experiences and enable data-driven decision-making to create a positive work environment, thereby contributing to the originality of HR research. Design/methodology/approach The study conducts sentiment analysis – a text mining technique – to assess employee reviews and extract distinct positive experience factors. The employed data-driven methodology serves to fortify the reliability and objectivity of the analysis, ultimately resulting in a more refined depiction of the conveyed sentiment. Findings Utilizing sentiment analysis, the authors identified 135 keywords that signify positive employee experiences. These keywords were then categorized into four clusters aligned with factors influencing employee experience: work, relationships, organizational system and organizational culture, employing an inductive approach. The framework outlines the process of nurturing positive employee experiences throughout the employee life cycle, incorporating insights from the affective events theory and cognitive appraisal theory. Practical implications Data-driven insights empower HR professionals to enhance employee satisfaction, engagement and productivity. HR managers implementing AI-assisted HR ecosystems need digital and data science skills. Additionally, these insights can offer practical support in accentuating diversity and ethical considerations within the organizational culture. Candid employee data can enhance leadership and support diversity in organizational culture. Managers play a crucial communication role, ensuring flexible access to personalized HR solutions. Originality/value Applying sentiment analysis through opinion mining allows for the collection of unstructured data, reflecting authentic employee perceptions. This innovative approach expedites issue identification and targeted actions, enhancing employee satisfaction. Textual reviews, integral to employee feedback, offer comprehensive insights. Additionally, considering subjectivity and review length in online employee reviews adds value to understanding experiences (Zhao et al., 2019). This study surpasses prior research by directly identifying key factors of employee experience through the analysis of actual employee review texts, addressing a gap in understanding beyond previous attempts.

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

The rapid growth of the video game industry and its reliance on digital distribution have created new opportunities for data-driven sales forecasting. Social media platforms serve as influential environments where consumer sentiment, trends, and discussions impact purchasing behaviors. This study examines the potential of using sentiment analysis of social media data to predict video game sales. While traditional sales forecasting models mainly depend on historical sales data and statistical techniques, sentiment analysis offers real-time insights into consumer interest and market demand. This paper reviews existing research on video game sales prediction, the application of sentiment analysis in the gaming industry, and sentiment-based forecasting models in other domains. The findings highlight a significant research gap in applying sentiment analysis to video game sales forecasting, despite its demonstrated efficacy in related fields. The study emphasizes the advantages and challenges of integrating sentiment analysis with traditional forecasting methods and proposes future research directions to enhance predictive accuracy.

This paper presents an application of sentiment analysis on customer feedback data in the area of heavy equipment repair services. Sentiment analysis is used as a part of a framework for text mining-based Customer Loyalty Improvement Recommender System (CLIRS). In order to provide business users of the system with accurate predictions for customer satisfaction metrics, the original algorithm for the opinion mining needed to be improved. The paper presents the background of the proposed approach, the current techniques used to mine text data and existing applications of sentiment analysis. We propose an aspect-based, taxonomy-driven approach for customized sentiment analysis. The contribution of this paper is the implementation and evaluation of the proposed methods that improve the accuracy and coverage of the opinion mining algorithm. The improvements are illustrated with examples covered by the algorithm in the customer dataset. The application of the proposed methods resulted in increasing the algorithm’s accuracy from 92% to 96%, and coverage from 36% to 48%. This research is an attempt to handle well-known issues in natural language processing that are currently not handled by text mining algorithms, such as ambiguity and context, opinionated verbs/nouns, subject recognition from pronouns. This is significant because the proposed techniques are generalizable to any application that uses sentiment analysis algorithm.

The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.

Sentiment analysis is the most vital research field in the area of natural language processing and text mining. It is a powerful tool for entrepreneurs that introduces products upon online selling websites where customers can post their reviews. In this paper, we have taken the state-of-the-art research papers which are highly cited and analyse them to understand the different techniques and methods to perform sentiment analysis. As sentiment analysis is related to natural language processing it is having several issues of computation. We have discussed many different techniques and tried to suggest the best way to work with them. We have also discussed the different applications of sentiment analysis.

The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

The internet’s prevalence has increased the use of social media platforms for sharing peoples’ views, reviews, opinions, and sentiments. Consequently, sentiment analysis has become valuable to marketing, brand management, public policy, healthcare, and e-commerce. Panic detection systems refer to a system that utilizes sentiment analysis techniques to identify, quantify, and potentially track expressions of panic, fear, or related strong negative emotions within digital content. Sentiment analysis application can indicate a person’s attitude or cultural influences. The rise of Arabic digital content on social platforms presents unique challenges and opportunities for sentiment analysis. There are fewer Arabic sentiment lexicons, emotional connotations, and annotated corpora than in English. Therefore, this study applies machine learning and deep learning to identify sentiments within the IRAQI-Arabic panic detection systems. The proposed model is based on Residual Network (ResNet), MobileNet, and Convolutional Neural Network (CNN). A web scraping API collected COVID-19-related news from Google, Facebook, Twitter, and BBC News. The model used was retrained on the images and comments based on training, public, and private sub-sets. Bidirectional Encoder Representations from Transformers (BERT) were applied across SMOTE, Random oversampling, five-folds, and ten folds. The results show that the accuracy of BERT is 91%. The proposed MobileNet model had an accuracy of 0.98, recall of 0.98, and MAE of 0.032. The model’s performance confirms that it is suitable for critical applications that require high-level precision. The study offers a novel model for Arabic panic systems. Also, it presents an Iraqi-Arabic dataset tailored for panic detection.

The application of sentiment analysis, also known as opinion mining, is more difficult in Chinese than in Indo-European languages, due to the compounding nature of Chinese words and phrases, and relatively lack of reliable resources in Chinese. This study used seed words, Chinese morphemes, which are mono-syllabic characters that function as individual words or be combined to create Chinese words and phrases, to classify movie reviews found on Yahoo! Taiwan. We utilized higher Pointwise Mutual Information (PMI) collocations, which consist of selected morpheme-level compounded features to build classifiers. The contributions of this study include the following: (Bird 2006) proposing a method of generating domain-dependent Chinese morphemes directly from large data set without any predefined sentimental resources; (Bradley and Lang 1999) building morpheme-based classifiers applicable in various movie genres, and shown to produce better results than other classifiers based on keywords (NTUSD and HowNet) or feature selection (TFIDF); (Church and Hanks in Computational linguistics, 16(1), 22-29 1990) identifying compounds that have different semantic polarities depending on contexts.

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

Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for the translation of texts of social media. More precisely, this paper focuses on the Tunisian Dialect of Arabic (TAD) with an application on automatic machine translation for a social media text into MSA and any other target language. Linguistic tools such as a bilingual TAD-MSA lexicon and a set of grammatical mapping rules are collaboratively constructed and exploited in addition to a language model to produce MSA sentences of Tunisian dialectal sentences. This work is a first-step towards collaboratively constructed semantic and lexical resources for Arabic Social Media within the ASMAT (Arabic Social Media Analysis Tools) project.

PurposeThe purpose of this paper is to investigate whether machine learning induces gender biases in the sense of results that are more accurate for male authors or for female authors. It also investigates whether training separate male and female variants could improve the accuracy of machine learning for sentiment analysis.Design/methodology/approachThis paper uses ratings-balanced sets of reviews of restaurants and hotels (3 sets) to train algorithms with and without gender selection.FindingsAccuracy is higher on female-authored reviews than on male-authored reviews for all data sets, so applications of sentiment analysis using mixed gender data sets will over represent the opinions of women. Training on same gender data improves performance less than having additional data from both genders.Practical implicationsEnd users of sentiment analysis should be aware that its small gender biases can affect the conclusions drawn from it and apply correction factors when necessary. Users of systems that incorporate sentiment analysis should be aware that performance will vary by author gender. Developers do not need to create gender-specific algorithms unless they have more training data than their system can cope with.Originality/valueThis is the first demonstration of gender bias in machine learning sentiment analysis.