Textual Data Sources Research Articles

Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features

Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately.

Comparison of text information from information sources based on the cosine similarity algorithm

This article presents research aimed at developing an optimal concept of analysis and comparison of information sources based on large volumes of textual information using natural language processing (NLP) methods. The object of the study was Telegram news channels, which are used as sources of text data. Texts were pre-processed, including cleaning, tokenization, and lemmatization, to form a global dictionary consisting of unique words from all information sources. For each source, a vector representation of the texts was built, the dimension of which corresponds to the number of unique words in the global dictionary. The frequency of use of each word in the channel's texts was displayed in the corresponding positions of the vector. By applying the cosine similarity algorithm to pairs of vectors, a square matrix was obtained that demonstrates the degree of similarity between different sources. The results of the study show the effectiveness of the proposed approach for quantitative assessment of the similarity of textual data from different sources. The need for further optimization of the algorithm was identified, in particular by parameterization to achieve a balance between accuracy and computational cost, as well as the separation of words with excessive weight, such as specific terms or channel names. The proposed method can be applied to the analysis of information flows, the identification of relationships between sources and the study of the socio-cultural influence of media content in the conditions of the modern information environment.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Named Entity Recognition Using Conditional Random Fields for Flood Detection In Gerbang Kertosusila Based Twitter Data

The national strategic area Gerbang Kertosusila East Java should be aware of floods. One of the existing efforts is to place flood sensors at several flood-prone points. However, that way is constrained by the need for more equipment to handle the many needy areas. So it is necessary to develop technology for the dissemination of flood information. Dissemination of flood information was quickly obtained from social media Twitter. One way is to use Twitter's text data source for a Named Entity Recognition model to help detect flood events and their locations. The Named Entity Recognition (NER) model was constructed using the Conditional Random Fields (CRFs) method to achieve research objectives. This research adds slang word handling at the preprocessing stage to improve model performance and the use of the BIO format in the labeling process and POS Tagging in the Feature Extraction process. Evaluation results with five Kfolds, 80% training data, and 20% test data show that the NER CRFs model performs excellently with a Precision of 0.981, Recall of 0.926, and f-measure of 0.950 so that these results can help the community and government regarding the information on the distribution of floods.

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices and they can take actions accordingly. The extraction of meaningful insights from the vast and dynamic stream of social media data is greatly facilitated by the implementation of topic modeling and sentiment analysis on Twitter tweets. The identification of recurring themes or subjects within a collection of tweets, which is the essence of topic modeling, serves to reveal patterns in discussions, thereby aiding in the comprehension of prevalent topics and trends. Concurrently, sentiment analysis, through the evaluation of the emotional tone of tweets, enables the discernment of whether the expressed sentiments are positive, negative, or neutral. The combination of these techniques provides researchers and businesses with valuable perspectives on public opinions, emerging issues, and user sentiments, thereby empowering them to make informed decisions and develop effective engagement strategies in the ever-evolving landscape of social media. But twitter data(tweets) are unstructured ,contains noisy data, urls , stop words, re-tweets video ,emoji etc., which need to be cleaned and preprocessed to perform proper sentiment analysis and use it to extract meaningful effective insights from it. This paper focuses on various methods of topic modeling and discovering latent topics ,text-mining approaches ,micro-blogging methods used in various researches .This paper focuses on latent dirichlet allocation method of topic modeling and text-mining to discover latent topics in tweets, micro-blogging ,text-mining approaches .A proper survey is done on previous researches on this topic in this article. Keywords:-LDA, text-mining, topic modeling, micro-blogging ,machine learning ,sentiment analysis, NLP.

Analyzing Twitter Users’ Sentiments on the Surge of Fuel Oil Prices in Indonesia using the K-Nearest Neighbor Algorithm

Sentiment analysis offers an effective solution for automating the classification of text data based on polarity, facilitating the assessment of public opinion. Among various social media platforms, Twitter stands out as a significant source of concise textual data reflecting users’ viewpoints on diverse topics. Notably, the recent surge in the price of fuel oil (BBM) in Indonesia has sparked considerable discussion and expression on Twitter. In this study, our objective was to perform a comprehensive sentiment analysis of Twitter users’ reactions to the rising fuel prices in Indonesia by employing the K-Nearest Neighbor (K-NN) algorithm. The research followed a structured approach encompassing data collection, text preprocessing, data labeling, feature extraction, data splitting, classification, and algorithm performance evaluation. The results revealed a dominance of negative sentiments among the 5,000 collected tweet data. The sentiments were categorized as 54.6% negative, 31.8% positive, and 13.6% neutral. This indicates a prevailing level of dissatisfaction and concern expressed by Twitter users regarding the fuel price increase. The K-NN algorithm’s classification performance was most promising when evaluated in an 80:20 data ratio experiment, yielding an accuracy rate of 65%, precision of 74%, recall of 45%, and an error rate of 35%. These findings suggest that the K-NN algorithm is valuable for effectively gauging public sentiment towards the escalating fuel prices in Indonesia. This research highlights the potential of sentiment analysis and the K-NN algorithm in assessing public reactions to significant events, providing valuable insights for policymakers and stakeholders in the energy sector.

Learning to Answer Visual Questions from Web Videos.

Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and prevents scalability. In this work, we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. Given narrated videos, we then automatically generate the HowToVQA69M dataset with 69M video-question-answer triplets. To handle the open vocabulary of diverse answers in this dataset, we propose a training procedure based on a contrastive loss between a video-question multi-modal transformer and an answer transformer. We introduce the zero-shot VideoQA task and the VideoQA feature probe evaluation setting and show excellent results. Furthermore, our method achieves competitive results on MSRVTT-QA, ActivityNet-QA, MSVD-QA and How2QA datasets. We also show that our approach generalizes to another source of web video and text data. We generate the WebVidVQA3M dataset from videos with alt-text annotations, and show its benefits for training VideoQA models. Finally, for a detailed evaluation we introduce iVQA, a new VideoQA dataset with reduced language bias and high-quality manual annotations.

Sentiment Analysis for Financial Markets

Abstract: Predicting movements in the stock market is a novel use of sentiment analysis's growing body of knowledge. The purpose of this study is to investigate the potential of NLP for predicting stock market movements by analyzing textual data sources such as news articles, social media posts, as well as earnings reports. The research examines current approaches, applications, and difficulties in sentiment analysis by drawing on extensive surveys and reviews [1], [2]. It also investigates the use of pre-trained models in NLP and the potential biases of such models [6]. Important research findings [3], [17] suggest that NLP-based sentiment analysis models, especially those employing deep learning architectures, show promising results in financial forecasting. There are, however, several difficulties associated with these models. These include the requirement of huge datasets and the elimination of biases. This study has far-reaching ramifications. One benefit is a more nuanced comprehension of the potential and pitfalls of natural language processing for sentiment analysis in the financial markets, which is useful for both analysts and investors. Second, it provides opportunities for more study to enhance the precision and dependability of such models, which ultimately aids in the development of more steady and well-informed monetary judgements.

Ensemble Text Summarization Model for COVID-19-Associated Datasets

The work of text summarization in question-and-answer systems has gained tremendous popularity recently and has influenced numerous real-world applications for efficient decision-making processes. In this regard, the exponential growth of COVID-19-related healthcare records has necessitated the extraction of fine-grained results to forecast or estimate the potential course of the disease. Machine learning and deep learning models are frequently used to extract relevant insights from textual data sources. However, in order to summarize the textual information relevant to coronavirus, we have concentrated on a number of natural language processing (NLP) models in this research, including Bidirectional Encoder Representations of Transformers (BERT), Sequence-to-Sequence, and Attention models. This ensemble model is built on the previously mentioned models, which primarily concentrate on the segmented context terms included in the textual input. Most crucially, this research has concentrated on two key variations: grouping-related sentences using hierarchical clustering approaches and the distributional semantics of the terms found in the COVID-19 dataset. The gist evaluation (ROUGE) score result shows a significant and respectable accuracy of 0.40 average recalls.

Exploring the knowledge structure and potential research areas of sustainable tourism in sustainable development: Based on text mining and semantic network analysis

AbstractThe concept of sustainable tourism development is derived from sustainable development. Building tourism with the framework of sustainable development has become a necessary effective way. This study examined the abstracts and titles of 1783 sustainable development and 692 sustainable tourism development related papers from 2012 to 2021 as textual data sources through the Web of Science core database. Using text mining and semantic network as analysis methods, this paper summarizes eight main clusters of sustainable development and sustainable tourism development in the past 10 years. Based on this, this paper compares the differences between the two researches and explores four potential research areas in sustainable tourism development: (1) threats to sustainability; (2) ecology and health; (3) stakeholder cooperation and roles; and (4) tourism development planning. In addition, the visual network knowledge structure intuitively captures the relationship between the different keywords in the clustering, which provides an important reference value for broadening the future research direction of sustainable tourism development.

Abstract 15133: Topic Models Using Notes From Electronic Medical Records Can Classify Non ST Elevation Myocardial Infarction Patients Based on the Presence of an Occluded Culprit Artery

Introduction: Approximately 33% of patients with a Non-ST-Elevation Myocardial Infarction (NSTEMI) have an occluded culprit artery which doubles their odds of adverse outcomes. Earlier identification of NSTEMI patients with occluded culprit artery may reduce their odds of adverse outcomes. Notes from the electronic health record (EHR) are rich sources of text data about signs and symptoms. Organizing text data into topics may help providers identify patients with NSTEMI and an occluded culprit artery sooner. Latent Dirichlet Allocation uses a deep-learning network to generate topics that can classify patients. Methods: Notes transcribed during the first 72 hours of hospitalization were extracted from the EHR among NSTEMI patients who presented to a medical center between 2015-2020. The data was preprocessed by removing punctuation, numbers, and stop words. We decided 5 topics based on computed coherence value. Two authors determined whether the first 100 terms were associated with an occluded artery. Analyses were completed in R using packages topicmodels and tidyverse . Results: We examined 45,025 notes from 2,223 patients (age=69 + 14 years old; 57% n=1267 male; 83% n=1,845 White) who experienced 2,685 hospitalizations (17%, n=462 NSTEMI re-hospitalizations). In Topic 1, 70% of the terms were associated with an occluded culprit artery including “chest pain” and “troponin”. In contrast, in Topic 5, 90% of terms were not associated with an occluded culprit artery such as “edema” and “pulmonary”. Figure 1 shows the frequency of terms associated with an occluded artery per topic. Conclusions: Topic 1 is most associated with an occluded culprit artery. Topic 5 is the least associated with an occluded culprit artery, instead associated with heart failure. Our results demonstrate it is feasible to develop topics to differentiate NSTEMI with and without an occluded culprit artery. Future research aims to identify patterns between signs and symptoms within topics.

Emotional Responses to Religious Conversion: Insights from Machine Learning

This study aims to understand the feelings of newly converted Muslims when they narrated their pre- and post-conversion using the Machine Learning model and qualitative approach. The data set analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly converted Muslims from various backgrounds. All recorded interviews were transcribed and filtered to remove any unnecessary or misaligned data to ensure that the data was fully aligned with the interview questions. To analyze emotional changes, we utilize natural language processing (NLP) algorithms, which enable us to extract and interpret emotional content from textual data sources, such as personal narratives. The analysis was performed in Google Colab and utilizing XLM-EMO, a fine-tuned multilingual emotion detection model that detects joy, anger, fear, and sadness emotions from text. The model was chosen because it supports Bahasa, as our interview was conducted in Bahasa. Furthermore, the model also has the best accuracy amongst its competitors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLM-RoBERTa-large, XLM-RoBERTa-base, and XLM-Twitter-base are .86, .81, and .84. Furthermore, two psychologists compared emotion detection results from the XLM-EMO model to the raw input data, and an inductive content analysis was performed. This approach allowed us to identify the reasoning behind the emotions deemed pertinent and intriguing for our investigation. This study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context. On the other hand, joy emerges as the most dominant, constituting a substantial proportion of 57.73% among the emotions analyzed from post-conversion emotions data. Understanding the positive impact of religious conversion on emotions may inform mental health interventions and incorporate spiritual or religious elements into therapeutic approaches for individuals struggling with emotional issues, guiding individuals undergoing religious conversion and emphasizing the potential emotional benefits.

Techniques, Methods, and Analysis of Text Mining: A Review

Recent technological advances have led to the availability of new types of observations and measurements that were previously not available and that have fueled the ‘Big Data’ trend. Along with standard structured forms of data (containing mainly numbers), modern databases include new forms of unstructured data comprising words, images, sounds, and videos which require new techniques to be exploited and interpreted. This study focuses on Text Mining, which is a set of statistical and computer science techniques specifically developed to analyze text data. New sources of text data are now available, such as text messaging, social media activity, blogs, and web searches. The increasing availability of published text, sophisticated technologies, and growing interest in organizations in extracting information from text have led to replacing (or at least supplementing) the human effort with automatic systems. Text mining can be used for a variety of scopes, ranging from basic descriptions of text content through word counts to more sophisticated uses such as finding links between authors and evaluating the content of scripts (e.g., automated marking of essays). Its basic purpose is to process the unstructured information contained in text data in order to make text accessible to various Data mining statistical algorithms. This could help make text data as informative as standard structured data and allow us to investigate relationships and patterns that would otherwise be extremely difficult, if not impossible, to discover. This study takes a quick look at how to organize and analyze unstructured text data using R programming language. And implementing various text mining operations to clean and structure the “eng_news_2020” dataset. This study also represents some association between the words using the chi-square test and clustering procedure.

Leveraging large language models to monitor climate technology innovation

To achieve net-zero emissions, public policy needs to foster rapid innovation of climate technologies. However, there is a scarcity of comprehensive and up-to-date evidence to guide policymaking by monitoring climate innovation systems. This is notable, especially at the center of the innovation process, where nascent inventions transition into profitable and scalable market solutions. Here, we discuss the potential of large language models (LLMs) to monitor climate technology innovation. By analyzing large pools of unstructured text data sources, such as company reports and social media, LLMs can automate information retrieval processes and thereby improve existing monitoring in terms of cost-effectiveness, timeliness, and comprehensiveness. In this perspective, we show how LLMs can play a crucial role in informing innovation policy for the energy transition by highlighting promising use cases and prevailing challenges for research and policy.

A cognitive analysis-based key concepts derivation approach for product design

In the development of a new product, concept generation is a crucial stage, as it determines much of the cost of the product lifetime. The process of generating concepts that meet customer requirements, however, can be challenging due to incomplete information, chaotic design constraints, and complicated design iterations. Deep learning-based pretrained language models can be a potential solution to overcome these limitations. But current methods are limited by the gap between the language used in design-related documents such as patent and the casual spoken language as the latter reflects the thinking process of concept generation. In this paper, we propose a key concepts derivation approach based on cognitive analysis to address challenges in product design and to effectively incorporate prior knowledge. This approach leverages a pretrained language model to extract information from multiple sources of textual data and represents design thinking in a vector space. We conduct a Design-by-Analogy based verbal protocol analysis experiment for high-speed elevators to collect verbal data and establish a design thinking corpus. Based on this, our approach is employed for key concepts derivation in product design and explore how solutions are generated to realize specific customer-required functionality. By analyzing the types of design stimuli, the effectiveness of our approach in deriving key design concepts are demonstrated with respect to novelty and feasibility.

Quantifying economic policy: Unsupervised learning on archival evidence from the United Kingdom, 1983–2021

AbstractThe evolution of economic policy in Western countries in the post‐1980 era is subject to extensive academic debate, but statistical modelling of its many qualitative aspects can be challenging. I use two sources of textual data from the United Kingdom – policy documents written by executive departments, as well as recently declassified cabinet minutes – together with unsupervised text‐as‐data methods to examine the evolution of economic policy discourse between 1983 and 2021. The findings challenge the hypothesis of an undifferentiated post‐1980 liberal era. Instead, several shifts away from the liberalizing priorities of the 1980s are identified. The first is an increased attention to the public services in the 1990s. The second is a rise of activist approaches focused on state‐supported innovation in the 2010s – a claim which has been widely articulated but has not been rigorously tested so far. These discourse‐based conclusions are validated through an econometric analysis of detailed spending data.

On the frontiers of Twitter data and sentiment analysis in election prediction: a review.

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

Assessment of text-generated supply chain risks considering news and social media during disruptive events

Information flow is an important task in a supply chain network. Disruptive events often impede this flow due to confounding factors, which may not be identified immediately. The objective of this study is to assess supply chain risks by detecting significant risks, examining risk variations across different time phases and establishing risk sentiment relationships utilizing textual data. We examined two disruptive events—coronavirus disease 2019 (Omicron phase) and the Ukraine–Russia war—between November 2021 and April 2022. Data sources included news media and Twitter. The Latent Dirichlet Allocation algorithm was applied to the textual data to extract potential text-generated risks in the form of “topics.” A proportion of these risks were analyzed to assess their time-varying nature. Natural language processing-based sentiment analysis was applied to these risks to infer the sentiment coming from the media using the ordered probit model. The results identify various unnoticed risks, for example: logistics tension, supply chain resiliency, ripple effect, regional supply chain, etc. that may adversely affect supply chain operations if not considered. The outcomes also indicate that textual data sources are capable of capturing risks before the events actually occur. The outcomes further suggest that text data could be valuable for strategic decision making and improving supply chain visibility.

TPTS: Text pre-processing Techniques for Sindhi Language

The Internet is a significant source of textual data, with users generating vast amounts of information through social media and news agencies daily. The extraction of meaningful information from large datasets is a challenging and costly process. Text pre-processing is a crucial initial step in any Natural Language Processing (NLP) task, as it can impact the overall performance of the study. The main objective of text pre-processing is to transform unstructured text into a linguistically meaningful (standard form) format, making extracting information for any text-processing task easier. This paper introduces TPTS, a model for text pre-processing in the Sindhi language. TPTS performs essential NLP tasks such as text tokenization, normalization, stop-word removal, stemming, and POS tagging for the Sindhi language. The Sindhi Text Corpus (STC), consisting of 1.5k Sindhi text documents collected from various online news websites, is used for experimentation. The TF-IDF approach is employed to identify high-frequency stop-words in the Sindhi language. Furthermore, a rule-based system tags words with their part of speech in Sindhi input text. The ROUGE evaluation metric is used to assess the effectiveness of the proposed TPTS technique, achieving 89% accuracy on the STC corpus. The Sindhi language is spoken by over 30 million people globally, and the lack of adequate NLP tools and resources limits the development of technology and natural language applications that can benefit Sindhi speakers. The proposed TPTS model can aid in developing such applications, making it beneficial not only for text pre-processing tasks but also for other Sindhi language text-processing tasks such as text summarization, sentiment analysis, speech-processing applications, text mining, and information retrieval systems.

Time and space as two basic attributes of Buddhist monuments: An introduction to the design, implementation, and application of the data platform of Buddhist monuments in China

Abstract The Data Platform of Buddhist Monuments in Time and Space (buddhist.wiki) is a research infrastructure that focuses on collecting and presenting related materials of Buddhist monuments, which are presented by Geo map and complete archive from five aspects: overview, architecture, history, art, and personage. Time and space are extracted as two basic attributes and applied as design dimensions to organize the order of huge amounts of graphic and text data, which provide systematic convenience for the collection, presentation, search, and research for the data platform. After a preliminary introduction to the importance of Buddhist monuments, data characteristics, and construction purposes of the data platform, this article introduces its design, implementation, and application from three aspects: first, the graphic and text data source characteristics related to Buddhist monuments are investigated to analyse how time and space (data attributes) are used as design dimensions to achieve high-degree data collection and presentation integration; secondly, data platform implementation is introduced in detail, including data acquisition, data storage design, and system design; finally, Tiantong Si is taken as an example to specifically introduce how time and space affect data platform design methods and participate in specific applications.