Anatomy of Sentiment Analysis in Ontological, Epistemological, and Axiological Perspectives

The study was a qualitative research, employing ontological and epistemological approaches to explore the philosophy of global warming. Questionnaires and interviews were conducted with students and teachers, which examined whether the current implementation of global warming education aligns with ontological and epistemological perspective and propose recommendations to improve global warming education in science lesson. The ontological perspective begins with literature review that examines the unique of greenhouse gasses characterized as heat traps, focusing on microscopic view of vibrational modes of molecules and their impact on global warming. Furthermore, the epistemological perspective explored how researchers validate the scientific understanding of global warming and its impact on the environment. In addition, the questionnaire result showed that various teaching method, such as experiment, video, PhET simulation, teacher explanation, and internet exploration, were used. But most of students never see the microscopic view of vibrational greenhouse gas molecules that trap the heat. Students also did not evaluate and validate the influence of greenhouse gas concentration on increasing system temperature as epistemological approach. Therefore, the lesson of global warming especially the concept of greenhouse effect, needs an approach that integrates ontological and epistemological perspective, such integrating modeling or simulation in microscopic scale into experimental activities (bifocal modeling).

Learning geometry from an epistemological philosophy perspective for elementary school students is very important to implement. This research aims to provide an overview of the basic concepts of geometry in the basic education curriculum. Epistemology philosophical theory relevant to geometry learning. The practice of integrating philosophy of epistemology in geometry education for elementary school or Madrasah Ibtidaiyah students. The research method used is a qualitative research method, emphasizing data collection and descriptive data analysis. The data collection method is by collecting and selecting journal articles, books, papers that are relevant to the research topic. Data is collected and analyzed thematically according to relevant topics. The results of the research show that learning geometry is very important for elementary school and Madrasah Ibtidaiyah students. The concept of epistemological philosophy encourages students to think critically. The practice of teaching geometry from an epistemological perspective using activities to explore geometric concepts through sensory experiences is very suitable for elementary school or Madrash Ibtidaiyah students. From this research it can be concluded that this learning is able to help students form attitudes in behavior and critical thinking skills.

Sentiment analysis (SA) is a process of extensive exploration of data stored on the Web to identify and categorize the views expressed in a part of the text. The intended outcome of this process is to assess the author attitude toward a particular topic, movie, product, etc. The result is positive, negative, or neutral. These study illustrated different techniques in SA approach for extracting and analytics sentiments associated with the polarity of positive, negative, or neutral on the topic selected. Social networks SA can be a useful source of information and data. SA acquires important in many areas of business, politics, and thought. So, this study contains a comprehensive overview of the most important studies in this field from the past to the recent studies till 2017. The main aim of this study is to provide full concept about SA techniques and its classification and methods used it. Also, we give a brief overview of big data techniques and its relation and use in SA field. Because the recent period has witnessed a remarkable development in the use of Big Data (Hadoop) in the process collection of data and reviews from social networks for analysis.

The proliferation of mobile web services (MWS) for sentiment analysis makes it hard to identify the best MWS for sentiment analysis of social networking sites’ fanatical contents. This paper carries out a comparative study of service-based sentiment analysis of social networking sites’ fanatical contents. This is achieved by cleaning, transformation, and reduction of fanatical contents from the publicly available social media dataset, and multiple MWS are selected for comparison using the application programming interface (API) key of the MWS. To evaluate the service-based sentiment analysis, standard measures such as accuracy, precision, recall, and f-measures of sentiment result for each MWS are used. The result shows that Dandelion SA performs better in terms of accuracy (72.5%) and recall (76.9%), while Wingify SA performs better in terms of precision (88.6%) and f-measure (75.5%), though AlchemyAPI offers the most crucial elements in analyzing sentiments such as emotion, relevance score, and sentiment type. The outcomes of this paper will benefit the sentiment analysis service developers, sentiment analysis service requesters as well as other researchers in the social media fanatical content domain.KeywordsSentiment analysisMobile web serviceSocial mediaFanatical contents

Influential user weighted sentiment analysis on topic based microblogging community

Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model.

To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

With the explosive growth of social media, the online community can freely express their opinions without disclosing their identities. People with hidden agendas can easily post fake opinions to discredit target products, services, politicians, or organizations. With these big data, monitoring opinions and distilling their sentiments remain a formidable task because of the proliferation of diverse sites with a large volume of opinions that are portrayed in multilingual. Therefore, this paper aims to provide a systematic literature review on multilingual sentiment analysis, which summarises the common languages supported in multilingual sentiment analysis, pre-processing techniques, existing sentiment analysis approaches, and evaluation models that have been used for multilingual sentiment analysis. By following the systematic literature review, the findings revealed, most of the models supported two languages, and English is seen as the most used language in sentiment analysis studies. None of the reviewed literature has catered the combination of languages for English, Chinese, Malay, and Hindi language on multilingual sentiment analysis. The common pre-processing techniques for the multilingual domain are tokenization, normalization, capitalization, N-gram, and machine translation. Meanwhile, the sentiment analysis classification techniques for multilingual sentiment are hybrid sentiment analysis, which includes localized language analysis, unsupervised topic clustering, and then followed by multilingual sentiment analysis. In terms of evaluation, most of the studies used precision, recall, and accuracy as the benchmark for the results.

Sentiment analysis automatically evaluates people’s opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis techniques for under-resourced languages and the use of high-resourced languages to develop resources for low-resource languages. The ultimate goal is to identify appropriate strategies for future investigations. We report over 35 studies with different languages demonstrating an interest in developing models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for sentiment analysis. Our focus is to critically compare methods, employed datasets and identify research gaps. This study contributes to theoretical literature reviews with complete coverage of multilingual sentiment analysis studies from 2008 to date. Furthermore, we demonstrate how sentiment analysis studies have grown tremendously. Finally, because most studies propose methods based on deep learning approaches, we offer a deep learning framework for multilingual sentiment analysis that does not rely on the machine translation system. According to the meta-analysis protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the sentiment analysis performance. Therefore, deep learning methods are recommended for the development of multilingual sentiment analysis for under-resourced languages.

Users can freely express their opinions about many events on social media platforms. It may be necessary to analyze the data in order to get the opinion of the society about these events. Therefore, sentiment analysis studies are gaining importance today. Many different methods and models are used for sentiment analysis. While language models such as the BERT model are widely used in the English language, there are very few studies for the Turkish language in sentiment analysis. In this study, sentiment analysis was performed on tweets using BERT models and machine learning methods. In addition, the trained BERT models and machine learning methods were compared. Among the Random Forest, Naive Bayes and Logistic Regression machine learning methods, Logistic Regression was the most successful method with 98.4%. BERT models achieved 98.75% accuracy and surpassed the success of machine learning methods. The positive effect of the BERT model on sentiment analysis was shown with this study.

Internet is a very rich resource of documents that need to be analysed to extract their sentimental values. Sentiment Analysis which is a subfield of Natural Language Processing discipline focuses on this issue. The existence of sentiment lexicons in their own language is a very important resource for scientists studying in sentiment analysis field. Since many studies of sentiment analysis have been conducted on text written in English language, developed methods and resources for English may not produce the desired results in other languages. In Turkish, a rich sentiment lexicon does not exists, such as SentiWordNet for English. In this study, we aimed to develop Turkish sentiment lexicon, and we enhanced an existing lexicon which has 27K Turkish words to 37K words. For quantifying the performance of this enhanced lexicon, we tested both lexicons on domain independent news texts. The accuracy of determining the polarity of news written in Turkish has been increased from 60.6% to 72.2%.

There has been substantial progress in the field of text based sentiment analysis but little effort has been made to incorporate other modalities. Previous work in sentiment analysis has shown that using multimodal data yields to more accurate models of sentiment. Efforts have been made towards expressing sentiment as a spectrum of intensity rather than just positive or negative. Such models are useful not only for detection of positivity or negativity, but also giving out a score of how positive or negative a statement is. Based on the state of the art studies in sentiment analysis, prediction in terms of sentiment score is still far from accurate, even in large datasets [27]. Another challenge in sentiment analysis is dealing with small segments or micro opinions as they carry less context than large segments thus making analysis of the sentiment harder. This paper presents a Ph.D. thesis shaped towards comprehensive studies in multimodal micro-opinion sentiment intensity analysis.

To determine whether a document or a sentence expresses a positive or negative sentiment, three main approaches are commonly used: the lexicon-based approach, corpus-based approach, and a hybrid approach. The study of sentiment analysis in English has the highest number of sentiment analysis studies, while research is more limited for other languages, including Arabic and its dialects. Lexicon based approaches need annotated sentiment lexicons (containing the valence and intensity of its terms and expressions). Corpus-based sentiment analysis requires annotated sentences. One of the significant problems related to the treatment of Arabic and its dialects is the lack of these resources. We present in this survey the most recent resources and advances that have been done for Arabic sentiment analysis. This survey presents recent work (where the majority of these works are between 2015 and 2019). These works are classified by category (survey work or contribution work). For contribution work, we focus on the construction of sentiment lexicon and corpus. We also describe emergent trends related to Arabic sentiment analysis, principally associated with the use of deep learning techniques.

The contribution continues the previous section entitled Concept of logistics service, where the logistics of services is characterized as a special part of the logistics and defined the essence of the services and especially logistics of services. The contirbution deals with the classification and description of logistics of services in various fields. The problem, which the article is devoted to, is the identification and classification of logistics of services in various fields of industry and non-manufacturing sector. The aim is to identify and describe logistics of services in various fields ranging from industry over health care to transportation. Individual fields can be further characterized and specially study, on the logistic side as well as on the logistics of services side. The main orientation for the analysis of the areas is in the logistics of services. Services are an inseparable aspect of human life and the more advanced company, the bigger interest for services. The same is true for the companies from various fields. The richer and more advanced companies, the bigger interest in ensuring their faultless service and professional operation and logistics of services not excluding.

The aim of this paper is to clarify the philosophical underpinnings of concepts and concept analysis and the implications of their use through the lens of particular ontological perspectives. Information on the philosophical foundations of concepts from an ontological and epistemological perspective is not readily identifiable in the international literature. Although some authors have made reference to the ontological perspectives of specific concept analysis processes, none have addressed the implications of the realist or relativist perspective in relation either to the analysis process or the implications of a particular ontological perspective on the meaning and utility of a specific concept. We describe the evolution of concept analysis and influence of ontological paradigms on specific analysis methods. Using an historical review of concept development within nursing thought, we decode the language of concepts and processes of concept analysis, outline the importance of the ontological foundation of concept development, and describe the impact of concept use. The nursing literature is dominated by concepts created from a realist perspective. Although recent nurse-authors have introduced evidence-based data to facilitate the development of a number of concepts, they have held fast to the perception that the 'best', most adequate or mature concepts transcend context. The theoretical shift from context-bound empirical analysis of concepts belies the complexity of nurses' work. Concepts are unapologetically context-bound. A concept that transcends context (based on realist ontology) will remain the same even when the context of praxis changes limiting its utility.