Text Analysis Techniques Research Articles

A Framework for Sentiment Analysis of Online News Articles

In many respects, the traditional way of life has altered as a result of the current innovation era. The vast amount of data that is being constantly and continuously disseminated by a large number of users via online journals, reviews, comments, news blogs, microblogging websites, social media, and other platforms has overflowed into information technology. Owing to the sheer volume of evaluation of valuable web assets, sentiment analysis is a major focus of the research. The process of identifying and removing abstract data from content using text analysis and natural language processing techniques is known as opinion analysis. News analysis can be used to map a company's behavior over time and provide important, useful information about companies. In order to characterize the general inclination or state of mind of customers as reflected in online life toward a specific brand or organization and determine whether they see positively or negatively, sentiment analysis is also useful in webbased life checking. For some users these days, reading the news online is a daily exercise. People's opinions will generally change depending on the news they experience. News reports on events involving emotions, whether positive, negative, or neutral. Sentiment analysis is a useful tool for studying the human feelings present in textual data. There are numerous difficulties in locating the sentiments news articles

Audience perceptions of athletes’ brand self-presentation on social media

ABSTRACT Research Question: Current literature on athlete branding on social media is divided into two streams: one explores athletes’ self-presentation strategies and the other investigates how external audiences perceive these brands. However, existing studies lack investigations that simultaneously examine athletes’ content strategies with audiences’ perceptions. Thus, this study aims to answer the following research question: How are female athlete brands’ self-presentation strategies perceived by their online audiences? Research Methods: We collected 1,903 posts from female athletes playing in the Italian women’s first football division (Serie A), employing the Model of Athlete Branding via Social Media (MABSM) to categorize their self-presentation strategies. Additionally, we quantified volume-based metrics of consumer engagement on social media (CESM) and analyzed the content of 44,190 comments through top-down and bottom-up automated textual analysis techniques. Results and Findings Female athletes are perceived as competing athletes when sharing posts about their sport or personal lives and as social media models when emphasizing their attractive appearance. Notably, some followers tend to perceive athletes in a sexually abusive manner, irrespective of the self-presentation strategy employed. In contrast to male athletes, content focusing on athletic performance generates a lower volume-based CESM in the female context. Implications: This research contributes valuable theoretical, methodological, and practical implications to the growing body of literature on athlete branding on social media. Specifically, our study highlights the importance of understanding the nuanced relationship between the types of content shared online by athlete brands and the resulting audience engagement, both in terms of volume and content.

Young adults’ pathway to digital entrepreneurship: from ideas to innovation

PurposeThis study aims to investigate the digital entrepreneurial intentions of Albanian youth, identify the obstacles they face in starting digital businesses and examine their preferences regarding the types of businesses they aspire to establish. The Theory of Planned Behavior (TPB) is used as a framework to analyze these factors.Design/methodology/approachPrimary data were collected via questionnaires distributed in public and private universities. In a sample of 325 students, Structural Equation Modeling with Confirmatory Factor Analysis, path analysis and machine learning-based text analysis were used.FindingsThis study reveals significant impacts of innovativeness, attitude towards entrepreneurship, subjective norms, perceived behavioral control and self-efficacy on digital entrepreneurial intentions among Albanian students. Additionally, text mining highlights a strong preference for digital entrepreneurship.Research limitations/implicationsThe theoretical contributions of this study include applying Structural Equation Modeling to reveal insights into the impact of entrepreneurial factors and obstacles. The findings can inform policymakers and educators in designing targeted interventions to support student entrepreneurship. Meanwhile, the limitations of this study encompass a small sample size, lack of time series and panel data and the absence of an evaluation of the impact of education system practices, along with the need to investigate the effects of young population emigration from Albania to the EU.Originality/valueThis research contributes to the understanding of digital entrepreneurial intentions and behavior by using TPB in the Albanian context, offering access to a diverse dataset from Albanian universities, testing the direct impact of innovativeness on entrepreneurial behavior and pioneering the use of machine learning techniques for text analysis. Thus, it provides novel insights into the entrepreneurial landscape in Albania. In addition, this work can drive initiatives to support student entrepreneurship and bridge the gap between academia and industry in Albania.

Experience in psychological counseling supported by artificial intelligence technology.

In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

Retracing digital assumptions in social movements: the Italian case of No Tav

This paper aims to shed light on the key role of digital repertoire in shaping participation forms, challenging internet as a flexible, dynamic, and pervasive platform that transcends reality in terms of sense and relationship construction processes. The research design traces the narration and performance patterns in the last decade of the No Tav movement case study. The work examines the concept of innovation as both an evolving repertoire and a set of users’ practices. The empirical analysis is conducted through digital content analysis using quantitative textual analysis techniques. The research questions aim to understand how activists produce and share knowledge, what mobilization patterns emerge and are enabled by digital media, and how organizational assumptions change due to the digital turn.

A Serious Matter: Using Computational Text Analysis to Examine Tech CEOs’ Communications About Artificial Intelligence Compared to Tech CEO and Select American Orators’ Communications Unrelated to Artificial Intelligence

This study relies on the use of computational text analysis techniques to conduct a thorough examination of the communications of high-profile, tech CEOs. I text mined 85 documents. My sample consists of CEO speeches and written announcements about AI (25 documents), tech CEO speeches and written announcements about topics unrelated to AI (25 documents), and finally speeches from American orators (35 documents). I followed the methods presented in Jockers' "Text Mining in R." I then collected and pre-processed the data set. I utilized various R packages to process the corpus, and ultimately conducted sentiment analyses, topic-modeling, and word frequency analyses. I also used Google Ngram Viewer to study the identified topics in a corpus of 8 million books. The results were striking. The sentiment analysis reveals that tech CEO discussions surrounding AI have a serious tone, contrasting with the neutral tone of discussions of general topics by tech CEOs and the enthusiastic sentiment of non-tech orators. The topic-modeling reveals distinct topics such as technology and innovation in the AI speeches, education-related discussions in non-AI speeches, and vision and luck in the American orator documents. The word frequency analysis reveals that the tech CEO, AI-related communications focused on “models” and “people.” The tech CEO, non-AI communications focus on “people” and “think.” The American orators’ communications centered on “men” and “war.” Overall, it appears that tech CEOs are concerned about AI’s impact on people. Non-tech leaders were concerned about uplifting people about the future.

Implementation of Think Pair and Share (TPS) Technique in Listening to Short Story Texts Learning

This research aims to determine whether there are differences in students' abilities in listening to short story texts using the Think-Pair-Share (TTPS) cooperative learning method. Conducted as a quasi-experimental study, the sample comprised 11th-grade students from SMA DDI Maros. Data collection involved text analysis techniques, and the obtained data were analyzed through stages including the creation of raw score lists, frequency distributions, descriptive statistical analysis, and inferential statistical analysis. The results indicate significant differences in learning outcomes between students using the TTPS method and those not using it. The use of TTPS improved students' listening comprehension of short story texts, as evidenced by the mean score of 74.36 (high category) in the experimental class compared to 58.46 (low category) in the control class. This demonstrates that the TTPS method effectively enhances students' listening skills.

A Systemic Functional Linguistic Analysis of Mood Structures in News Coverage of Anies Baswedan’s Speech During Presidential Election Debates in The Jakarta Post and TEMPO.CO

The purpose of this study is to use Halliday’s Systemic Functional Linguistics viewpoint to uncover the interpersonal meanings that are realized in five selected news articles from The Jakarta Post and TEMPO.CO. This study employed a qualitative method. The data source is two articles from The Jakarta Post and three articles from TEMPO.CO, all under the categories of the 2024 presidential election debate. The data were collected through textual analysis and close reading techniques. The results of this research are as follows: (1) The declarative mood is frequently used across all five articles with 97% for TJP and 99% for TC. The sentences are mostly realized by temporal deicticity, while modality appears in small percentages. (2) Both TJP and TC potray Anies Baswedan positively but with different emphases. TJP highlights Baswedan’s personal attributes and political background. TC emphasizes Baswedan’s political expertise and policy proposals. This distinction in focus reflects the different strategies employed by TJP and TC to shape public perception during the election period. TJP and TC employ similar tenor patterns where they position themselves as equals to their readers and conveying a positive tone in their interactions.

The interface between research funding and environmental policies in an emergent economy using neural topic modeling: Proposals for a research agenda

AbstractResearchers have been calling for further analysis of the relationships between academia and policy making. Since several studies are conducted with resources from public funding agencies, it is essential to assess whether governments have been supporting research that can contribute to future environmental policies and whether research has been supporting the elaboration of public policies. To fill these gaps, the objective of this study was to evaluate the similarities and gaps between research and environmental policies. A bibliometric analysis of studies funded by Chilean agencies and text analytics techniques were employed to analyze research and environmental policies. We used BERTopic, a topic modeling technique that generates coherent topic representations. Then, topics obtained from the two sets of documents (research and policies) were compared. This study found that sustainability, life‐cycle assessment, climate change, and ecosystem services are central research themes in the country. The topic modeling analysis showed that research has been concentrated on the areas of “Sustainable Fisheries and Aquaculture Management” and “Sustainable Bioenergy Production and Chemical Processes.” The topics most frequently present in the environmental policies were “Environmental Education for Children: Awareness and Care,” “Bird Conservation and Habitat Importance in Chile,” and “National Biodiversity Conservation and Sustainable Use Strategy.” The greatest gaps are observed in the clusters of “Sustainable Bioenergy Production and Chemical Processes” and “Copper Mining and Mineral Processing Techniques” (prevalent in research) and in “Anatomy and Physiology of Aquatic and Terrestrial Organisms” and “Biogeography and Taxonomy of Southern Hemisphere Insects” (prevalent in policy).

Word2Vec과 엔트로피를 활용한 연구동향 분석

To understand trends in a specific research field, qualitative literature analysis of major papers published in that field has traditionally been used. Recently, there has been an increasing effort to quantitatively analyze large volumes of literature using text analysis techniques. This study proposes an analysis method utilizing word embedding and entropy. The proposed method is applied to analyze literature in the field of Management Information Systems (MIS). For this purpose, 12,704 papers published in major MIS journals were collected. Word2Vec and annual keyword clustering were applied to 7,813 keywords extracted from these papers, and the research trends in the MIS field were analyzed through vector similarity analysis. Furthermore, research topics were visualized and classified according to their life cycles based on the rate of entropy change and average values. The method proposed in this study is applicable to various research fields and holds significance as a quantitative approach to analyzing large volumes of literature.

The Effect of Analysts’ Reports on Stock Liquidity: The Interaction of Ratings and Qualitative Indicators

We utilized a Python program to collect analysts’ reports on the constituents of the CSI 300 (China Securities Index). From these reports, we extracted analyst ratings, readability, and visualization using text analysis techniques. Additionally, a FinBERT model was employed to evaluate the tone and disagreement among analyst reports. Subsequently, the FGLS model was developed to explore the influence of analysts’ reports on stock liquidity. The analysis revealed a positive impact of analyst ratings on stock liquidity, with all qualitative indicators playing significant moderating roles. Specifically, there was a more pronounced response in stock liquidity to ratings when accompanied by higher readability, richer visualization, a more positive tone in the report, and lower disagreement. Our study highlights that investors consider both ratings and qualitative information from analyst reports, with the latter guiding investors on how to interpret ratings effectively. These findings have implications for investors, analysts, and regulators.

AlgoDM: algorithm to perform aspect-based sentiment analysis using IDistance matrix

Sentiment analysis is a method of analyzing data to identify its intent. It identifies the emotional tone of a text body. Aspect-based sentiment analysis is a text analysis technique that identifies the aspect and the sentiment associated with each aspect. Different organizations use aspect-based sentiment analysis to analyze opinions about a product, service, or idea. Traditional sentiment analysis methods analyze the complete text and assign a single sentiment label to it. They do not handle the tasks of aspect association, dealing with multiple aspects and inclusion of linguistic concepts together as a system. In this article, AlgoDM, an algorithm to perform aspect-based sentiment analysis is proposed. AlgoDM uses a novel concept of IDistance matrix to extract aspects, associate aspects with sentiment words, and determine the sentiment associated with each aspect. The IDistance matrix is constructed to calculate the distance between aspects and the words expressing the sentiment related to the aspect. It works at the sentence level and identifies the opinion expressed on each aspect appearing in the sentence. It also evaluates the overall sentiment expressed in the sentence. The proposed algorithm can perform sentiment analysis of any opinionated text.

Talking past each other: Institutional complexity in public projects

Large-scale public projects often experience delays and cost overruns, in part because they require extensive coordination between numerous organizations, both private and public. In this paper, we draw on the perspective of new institutionalism to explore differences between stakeholders in large-scale projects. We develop a topic modeling approach based on a state-of-the-art computational text analysis technique and apply it to a set of transcripts from parliamentary select committee hearings in the United Kingdom to uncover cultural-cognitive differences that reflect a variety of institutional logics. The application of this technique to four different projects reveals significant institutional differences between groups of actors. We argue that greater awareness of these institutional differences may support attempts to understand why large-scale projects experience such significant management challenges and how such challenges could be addressed. We use data for the period 2000–2019 from two different sectors, defence, and public transport.

IMPLEMENTATION OF LITERACY CULTURE IN VOCATIONAL SCHOOLS TO INCREASE STUDENTS' LEARNING MOTIVATION

This research aims to describe how literacy culture is implemented in vocational schools to increase student learning motivation. The research method uses participatory observation, where researchers directly observe daily activities in vocational schools related to the implementation of literacy culture. Respondents consisted of three teachers and 90 students from Computer Network and Telecommunications Engineering (TJKT) at SMKN 1 Gunungguruh Sukabumi. The research results show that by using a classical and independent approach, the school creates a learning environment that supports the development of students' literacy skills. The classical approach involves teachers in direct teaching and intensive guidance on reading, writing and text analysis techniques, while the independent approach provides opportunities for students for independent exploration in reading and writing. This combination not only improves students' literacy skills but also stimulates their creativity and increases motivation towards the learning process.

Leveraging Narrative Feedback in Programmatic Assessment: The Potential of Automated Text Analysis to Support Coaching and Decision-Making in Programmatic Assessment.

Current assessment approaches increasingly use narratives to support learning, coaching and high-stakes decision-making. Interpretation of narratives, however, can be challenging and time-consuming, potentially resulting in suboptimal or inadequate use of assessment data. Support for learners, coaches as well as decision-makers in the use and interpretation of these narratives therefore seems essential. We explored the utility of automated text analysis techniques to support interpretation of narrative assessment data, collected across 926 clinical assessments of 80 trainees, in an International Medical Graduates' licensing program in Australia. We employed topic modelling and sentiment analysis techniques to automatically identify predominant feedback themes as well as the sentiment polarity of feedback messages. We furthermore sought to examine the associations between feedback polarity, numerical performance scores, and overall judgments about task performance. Topic modelling yielded three distinctive feedback themes: Medical Skills, Knowledge, and Communication & Professionalism. The volume of feedback varied across topics and clinical settings, but assessors used more words when providing feedback to trainees who did not meet competence standards. Although sentiment polarity and performance scores did not seem to correlate at the level of single assessments, findings showed a strong positive correlation between the average performance scores and the average algorithmically assigned sentiment polarity. This study shows that use of automated text analysis techniques can pave the way for a more efficient, structured, and meaningful learning, coaching, and assessment experience for learners, coaches and decision-makers alike. When used appropriately, these techniques may facilitate more meaningful and in-depth conversations about assessment data, by supporting stakeholders in interpretation of large amounts of feedback. Future research is vital to fully unlock the potential of automated text analysis, to support meaningful integration into educational practices.

Framing Analysis of Increasing Fuel Prices News on cnnindonesia.com

This research discusses the framing of issues carried out by the online media cnnindonesia.com in reporting on the increase in fuel prices officially announced by President Joko Widodo. The aim of this research is to analyze the issue framing strategy used by cnnindonesia.com in packaging news about fuel price increases. This type of research is qualitative research using media text analysis techniques in the form of Robert N. Entman's framing model analysis of 18 news stories released by cnnindonesia.com on Saturday 3 September 2022. The results of the research show that the issue framing strategy used by cnnindonesia.com is in its reporting. provides a definition of the problem as a global economic problem and has an impact on social problems. An increase in the APBN is the cause of problems and places the government as an actor or source of problems. The moral decision was framed from the president's statement that it was difficult to make a decision regarding the increase in fuel prices and constructed the repressive actions of members of the National Police against demonstrators. Presenting a solution to the problem by framing a political narrative battle between the government and the opposition and explaining that the National Police Chief must be responsible for the repressive actions carried out by the police against students and workers in the action against the increase in fuel prices in all regions.

#Justice4Marielle: memory, datafication and justice among digital counterpublics that name Marielle Franco

This article seeks to examine the digital discursive practice of naming Marielle Franco on Twitter, mobilizing the perspectives and theoretical framework of Digital Humanities in the Global South, Data feminism and Datafication from Below. We also employ the methodology and theoretical framework of Digital Discourse Analysis and Critical Technocultural Discourse Analysis, integrating aspects of Performance Studies and Gender Studies to explore the relevance of memory and performance for contemporary digital counterpublics. We advance a technocultural discourse analysis of tweets that reference Marielle Franco between 2018 and 2023, in Spanish, English and Portuguese. We use a combination of techniques of computational textual analysis implemented in Wolfram Mathematica to study the chronological distribution of tweets; the emergence and relevance of hashtags; the users that were most frequently mentioned and the different topics they mobilised in social networks. Our conclusions point to the digital technocultural discourse on Marielle Franco as practices of memory and mourning that contribute to constitute a digital counterpublic in which naming, together with practices of datafication from below, are highly political, and serve not only to reassert collective identity but also to create a vocabulary to speak about the intersectional nature of oppression.

Automatic Algerian Sarcasm Detection from Texts and Images

In recent years, the number of Algerian Internet users has significantly increased, providing a valuable opportunity for collecting and utilizing opinions and sentiments expressed online. They now post not just texts but also images. However, to benefit from this wealth of information, it is crucial to address the challenge of sarcasm detection, which poses a limitation in sentiment analysis. Sarcasm often involves the use of non-literal and ambiguous language, making its detection complex. To enhance the quality and relevance of sentiment analysis, it is essential to develop effective methods for sarcasm detection. By overcoming this limitation, we can fully harness the expressed online opinions and benefit from their valuable insights for a better understanding of trends and sentiments among the Algerian public. In this work, our aim is to develop a comprehensive system that addresses sarcasm detection in Algerian dialect, encompassing both text and image analysis. We propose a hybrid approach that combines linguistic characteristics and machine learning techniques for text analysis. Additionally, for image analysis, we utilized the deep learning model VGG-19 for image classification, and employed the EasyOCR technique for Arabic text extraction. By integrating these approaches, we strive to create a robust system capable of detecting sarcasm in both textual and visual content in the Algerian dialect. Our system achieved an accuracy of 92.79% for the textual models and 89.28% for the visual model.

Qanā‘ah al-Ghazālī sebagai Landasan Stabilitas Mental: Pendekatan Holistik-Psikosufistik dengan Teknik Pernapasan 4-7-8

This article examines al-Ghazālī’s concept of qanā‘ah—contentment—as a pathway to mental health stability, offering insights into the wisdom and meaning behind applying Sufism and holistic therapeutic practices to address emotions such as anger, sadness, and anxiety. The 4-7-8 deep breathing technique is proposed as a complementary solution to these mental health challenges. The research adopts a psycho-sufistic approach to healing, focusing on the management of mental stability issues related to anger, anxiety, and sadness through the integration of Sufi qanā‘ah and the 4-7-8 breathing method, tailored for the modern era. A qualitative methodology with descriptive analysis is employed, drawing on library-based research. The data is sourced from various forms of documentation, including books, newspapers, transcripts, and other written materials, analyzed using text and discourse analysis techniques. The findings suggest that the application of psycho-sufistic qanā‘ah combined with the 4-7-8 breathing technique can effectively address mental stability issues, offering a means to alleviate anger, sadness, and anxiety in contemporary society. This holistic and Sufi-informed psychological approach aims to harmonize the heart and mind, fostering a more meaningful and fulfilling life for individuals in the modern age.