Raw Text Data Research Articles

Using large language models to create narrative events

Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts.

Text-to-Model Transformation: Natural Language-Based Model Generation Framework

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented.

Sentiment analysis of movie review classifications using deep learning approaches

Movie reviews reflect how the public feels about a movie they have watched. However, because many reviews are posted on various websites, it is practically impossible to read each one. Summarizing all movie reviews can help people make informed decisions without reading through all of them. Previous studies have used different machine learning and deep learning techniques for sentiment analysis (SA), but few have combined comprehensive hyperparameter tuning and novel datasets for better performance. This paper presents an SA approach using deep learning models with optimized hyperparameters and a novel Rotten Tomatoes (RT) dataset to help viewers make better movie choices. SA, or opinion mining, is a computational technique to extract and analyze opinions and emotions expressed in text. We explore deep learning models such as Long Short-Term Memory (LSTM), XLNet, Convolutional Neural Networks-LSTM (CNN-LSTM), and Bidirectional Encoder Representations from Transformers (BERT). These models are known for capturing complex language patterns and context from raw text data. XLNet, a pre-trained model, effectively understands context by considering all possible permutations of the input sequence, BERT excels at using bidirectional context to understand text, LSTM retains information about long-term patterns in sequential data, and CNN-LSTM combines local and global context for reliable feature extraction. The RT dataset was pre-processed with data cleaning, spelling correction, lemmatization, and handling of informal words to improve the results. Our experiments show that XLNet performed better than other models on the Rotten Tomatoes dataset. The study demonstrates that SA of movie reviews provides insights into emotions and attitudes, allowing us to estimate a movie’s performance based on its overall sentiment.

Text sentiment analysis and classification based on bidirectional long and short term memory networks

In this study, we explored the method of text sentiment analysis and classification using bi-directional long and short-term memory networks, which brings new ideas to the field of sentiment classification. In the text pre-processing stage, we processed chat phrases, non-alphanumeric characters, deactivated words, numbers, special characters, blanks, and addresses to convert the raw text data into numerical data that can be processed by the model. Subsequently, word frequency statistics were conducted for texts of different mood types to analyse the frequency of occurrence of different words in texts of different mood types. By introducing a bi-directional long and short-term memory network model for training, we observed the accuracy and loss value performance on the training and validation sets. The accuracy of the training set increases from the initial 94.6% to 99.5%, and the accuracy of the validation set gradually grows to 99.4%; at the same time, the loss value of the training set decreases from the initial value of 0.2075 to 0.0194, and the loss value of the validation set gradually increases from the initial value of 0.0538 to 0.0372. This indicates that the bi-directional long short-term memory network achieves a significant and stable performance improvement. The confusion matrix results for the test set show that our model achieves more than 99% prediction accuracy on the test set. This implies that our proposed method can efficiently classify textual sentiment with high prediction accuracy. This research result helps to improve the effectiveness of the application of sentiment analysis techniques in the fields of social media opinion monitoring and product review analysis, and provides a useful reference for further deepening the research on text sentiment analysis. In conclusion, this study achieves the optimisation of the text sentiment classification task by means of a bidirectional long and short-term memory network model, and achieves satisfactory results. In the future, the model performance and generalisation ability can be further improved by combining more corpus and optimising the model structure, so as to be better applied in real-world scenarios.

Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media

Messages shared on social media platforms like X are automatically categorized into two groups: those who self-report COVID-19 status and those who do not. However, it is essential to note that these messages cannot be a reliable monitoring tool for tracking the spread of the COVID-19 pandemic. The classification of social media messages can be achieved through the application of classification algorithms. Many deep learning-based algorithms, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM), have been used for text classification. However, CNN has limitations in understanding global context, while LSTM focuses more on understanding word-by-word sequences. Apart from that, both require a lot of data to learn. Currently, an algorithm is being developed for text classification that can cover the shortcomings of the previous algorithm, namely Bidirectional Encoder Representations from Transformers (BERT). Currently, there are many variants of BERT development. The primary objective of this study was to compare the effectiveness of two classification models, namely BERT and IndoBERT, in identifying self-report messages of COVID-19 status. Both BERT and IndoBERT models were evaluated using raw and preprocessed text data from X. The study's findings revealed that the IndoBERT model exhibited superior performance, achieving an accuracy rate of 94%, whereas the BERT model achieved a performance rate of 82%.

Analysis of Tonality of Text Using Machine Learning

Abstract: Tonality analysis using machine learning algorithms refers to the computational examination of written text with the objective of discerning and categorizing the emotional or subjective tone embedded within the content. In the realm of natural language processing (NLP), this analytical approach seeks to automate the process of understanding and classifying the sentiment expressed in textual data, facilitating a nuanced interpretation of language that extends beyond mere lexical meaning. At its core, the analysis of tonality involves the utilization of sophisticated machine learning models to distinguish and categorize the emotional underpinnings of text. This is particularly valuable in scenarios where vast amounts of unstructured textual data need to be processed, such as customer reviews, social media interactions, or news articles. The overarching goal is to automatically assign predefined tonal labels, such as positive, negative, or neutral, to segments of text, thereby enabling a more quantitative and systematic investigation of the subjective aspects of language. The process typically commences with the acquisition of raw textual data, which undergoes a series of preprocessing steps. These steps involve cleaning the text, breaking it into individual units (tokenization), and extracting relevant linguistic features. Feature extraction is a critical aspect, as it involves transforming the raw text into a format suitable for machine learning algorithms. This may encompass capturing word frequencies, utilizing n-grams to identify contextual patterns, and incorporating sentiment lexicon scores to gauge the emotions.

Analyzing Tourism Reviews using Deep Learning and AI to Predict Sentiments

In this study, we investigate the application of Artificial Intelligence (AI), specifically through Deep Learning and neural networks, in analyzing and predicting sentiments expressed in tourism reviews. Our dataset comprised various hotel reviews, with the objective to predict whether each textual review indicates positive or negative feedback. The primary challenge was to use solely the textual data of the reviews for this prediction. Through meticulous data processing and analysis, we developed neural network-based models that highlight the efficacy of Deep Learning in the accurate interpretation of reviews. Our findings reveal a significant correlation between the content of reviews and their overall ratings, thereby providing new insights into the application of AI in automating and enhancing understanding of customer needs and perceptions in the tourism sector. The principal contribution of this study is the practical demonstration of how AI techniques can be effectively employed to analyze large volumes of textual data, opening new avenues for marketing strategies and service optimization in the hospitality industry. Each review represents a client’s assessment of a hotel. For each textual review, we aim to predict whether it corresponds to a positive review (the customer is satisfied) or a negative review (the customer is dissatisfied). The overall ratings of the reviews can range from 2.5/10 to 10/10. To simplify the issue, we’ll categorize them as follows: negative reviews have overall ratings of less than 5; positive reviews have ratings of 5 or higher. The challenge lies in predicting this information using only the raw textual data of the review.

Text sentiment analysis using deep convolutional networks

The ability to process language is a difficult task. A computer system that can extract meaningful information from raw text data is considered to be equipped with intelligent behaviour. Sentiment analysis is a form of information extraction of growing research and commercial interest, especially because of the exponential increase of data in recent years. This paper presents a machine learning approach built upon Neural Networks that equips a computer system with the ability to learn how to detect sentiment in a given text and to correctly classify previously unencountered text with respect to predefined sentiment categories. The developed approach does not rely on any hard-coded parameters. Rather, a model is learned through a training procedure performed on raw data alone with the objective of searching to identify patterns and discovering feature predictors that allow the algorithm to classify text into positive, negative, or neutral sentiments, while additionally focusing on nuances such as humorous or sarcastic text. Experimental validation shows promising results. The learning algorithm is fed with high-volume textual data extracted from the Twitter social media platform. The trained model is then tested on a separate dataset, previously unknown by the model. The classification algorithm is able to predict the sentiment associated with a given text with high accuracy.

Pre-training model for low-resource Chinese–Braille translation

The technology for converting Chinese to Braille is of great importance. When paired with a Braille display, it can better meet the educational and daily needs of the visually impaired community, especially children and students. Incorporating visual assistance mechanisms can further enhance the user experience and provide comprehensive support for individuals with visual impairments. In recent years, the use of end-to-end neural machine translation models for Chinese–Braille translation has gained traction. However, this task requires large, high-quality, and domain-specific parallel data to train robust models. Unfortunately, the existing Chinese–Braille parallel data is insufficient to achieve satisfactory results. To address this challenge, this paper puts forward a groundbreaking approach that integrates pre-training models into the Chinese Braille translation task. This represents the first-ever application of such technology in this context and it is different from traditional pre-training methods. While previous pre-training method of natural language processing mainly utilized raw text data, we have identified its limitations in improving Chinese–Braille translation. Therefore, we have proposed three novel forms of pre-training datasets, instead of relying solely on raw text data. By utilizing the Transformer model, our approach achieves the highest BLEU score of 94.53 on a 10k parallel corpus, presenting a new direction for Chinese–Braille translation research. Furthermore, we introduce a new form of data that enables Chinese–Braille translation solely using the encoder framework. Leveraging the MacBERT model, this approach achieves a BLEU score of 98.87 on the test set and demonstrates an inference speed 54 times faster than the Transformer model. These findings have significant implications for the field of Chinese–Braille translation, providing insights for future research endeavors.

GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example

Data Scientists deal with a wide variety of file data formats and data representations. Probably the most difficult to handle are custom data formats that liberally define their own particular flat or nested structure with multiple custom delimiters, multi-line records, or undocumented semantics of attribute sequences, co-appearances, and repetitions. As a prerequisite for exploratory ML model training, data scientists need to map these data representations into regular frames or matrices. Unfortunately, existing tools and frameworks provide only limited support for aiding this process, which causes redundant manual efforts and unnecessary data quality issues. In this paper, we initiate work on automatic matrix and frame reader generation by example. A user provides a sample of raw text data and its mapped matrix or frame representation. Our GIO framework then first identifies the mapping rules from raw to structured data, and subsequently generates source code of an efficient, multi-threaded reader for reading full raw datasets of this format. In order to facilitate manual improvements, both the mapping rules, and generated reader can be modified as needed. Our experiments show that GIO is able to correctly identify the mapping rules for basic text formats like CSV, LibSVM, MatrixMarket; custom text formats from publishing, automotive, and health care; as well as various nested formats such as JSON and XML. Additionally, the automatically generated readers yield competitive performance compared to hand-coded readers and tuned libraries like RapidJSON.

A Voting classification approach for Sentiment Extraction from Bengali text

Sentiment extraction is one of the most challenging tasks in Natural Language Processing (NLP). It is essential for analysing consumer and user feedback on social media sites and in the commercial world. Finding sentiments or emotions in raw text data and identifying their polarity, or whether they are positive or negative, is the main objective of sentiment extraction. This area has been the focus of various research projects for English and other significant natural languages. In this article, we offer a voting classification method that uses a variety of machine learning classifiers to extract sentiment from Bengali language text. We explored Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier, Multinomial Nave Base and Ridge Classifier, and lastly, we used a voting classification strategy to extract sentiments from social media comments.

On semi-automated extraction of causal networks from raw text

A rule-based framework, named SCANER (Semi-automated CAusal Network Extraction from Raw text) is presented. SCANER converts raw text into causal networks using a set of natural language processing rules. The raw textual data that contains useful information for informing future decisions and developing strategies in various critical domains must be converted into useful, understandable models. However, building such models manually from raw text is a labor-intensive and time-consuming process and needs to be automated. The automated extraction of causality from raw text, however, has its own set of challenges due to the linguistic complexity and ambiguity of the texts. It can also be argued that for complicated scenarios and critical domains subject matter experts cannot rely solely on such automatically generated models without sound reasoning to support them. This implies the need for a semi-automated human-in-the-loop approach. This research, therefore, is focused on combining the strengths of both manual and automated approaches and on developing a framework for a rule-based semi-automated approach to generate causal networks from raw text. SCANER is evaluated on three collections of raw text from political, food insecurity, and medical domains using F-Score as the evaluation metric. The ground truth for the causal links is generated after incorporating feedback from a group of three human judges. A comparative analysis with Eidos, an open-source causality extraction system, demonstrates the advantages of SCANER in generating dense and accurate causal networks from the raw text.

SENTIMENT ANALYSIS ON COVID-19 RELATED TWEETS

1. Introduction COVID-19[1] is a pandemic going on during the current time. COVID-19 poses a new challenge to humankind. Meanwhile, It is essential to know what the majority of people thinking about this pandemic. Sentiment analysis[2] is a widespread technique applied in field of marketing, customer feedback and consumer research. This project performs sentiment analysis on COVID-19 related tweets[3] and getting some higher idea about people's sentiment toward pandemic. After performing sentimental analysis on COVID-19 related tweets, this project also draws some interesting findings and conclusions in the last section. Performing sentiment analysis on social media data like tweets, Facebook comments are challenging task for some reasons .1) Due to high volume, data may or may not be in csv, excel ,or other text-rich formats. Performing operations on data needs 2) To draw human sentiment from raw text itself complex task for a machine. However, recently there are advanced deep learning based methods available for performing such a complex task. For example, Answering the question by chatbot or auto-compilation of a statement in email.[4]. This project takes NLP(Natural Language Processing) approach with the help of Google's pre-trained deep neural network called BERT[5] for identifying sentiment from raw text data. The layers of deep neural network implemented in PyTorch[6].

A Survey of Text Representation and Embedding Techniques in NLP

Natural Language Processing (NLP) is a research field where a language in consideration is processed to understand its syntactic, semantic, and sentimental aspects. The advancement in the NLP area has helped solve problems in the domains such as Neural Machine Translation, Name Entity Recognition, Sentiment Analysis, and Chatbots, to name a few. The topic of NLP broadly consists of two main parts: the representation of the input text (raw data) into numerical format (vectors or matrix) and the design of models for processing the numerical data. This paper focuses on the former part and surveys how the NLP field has evolved from rule-based, statistical to more context-sensitive learned representations. For each embedding type, we list their representation, issues they addressed, limitations, and applications. This survey covers the history of text representations from the 1970s and onwards, from regular expressions to the latest vector representations used to encode the raw text data. It demonstrates how the NLP field progressed from where it could comprehend just bits and pieces to all the significant aspects of the text over time.

Ensemble Machine Learning Approach for Stress Detection in Social Media Texts

Stress is an almost basic human instinct, especially in the internet age. People’s health is being endangered by psychological stress. It is critical to diagnose stress early on to provide proactive therapy. In the age of the Internet, social media has an enormous impact on human thinking. It can cause mental health issues such as stress but can also play an important part in detecting it. Advancements in machine learning and natural language processing have enabled information extraction from a huge amount of raw textual data. In this study machine learning approach is used to detect stress in a social media text. Data used in this study is from Reddit. Classical and Ensemble machine learning approaches were used for stress detection. Classical Techniques include Decision Tree, Logistic Regression, support vector machine, Random Forest, and Na ̈ıve Bayes. Ensemble approaches used are boosting, bagging, and voting. The ensemble approach was able to provide better results than all machine learning baselines on the dataset. Also, it was able to outperform all non-transformed-based neural network architectures discussed in the baseline. The best-performing model in this study is Logistic regression with a 76.6% F1 Score.

Perceptions of orthopaedic medicine students and their supervisors about practice-based learning: an exploratory qualitative study

BackgroundPractice-based learning is crucial in forming appropriate strategies for improving learning among the medical students that support the country’s understaffed health sector. Unsatisfactory learning consequently results in poor performance of students and poor quality of health care workforce in the long run. Exploring the perceptions about the current practice-based learning system and how to improve is thus vital. This study set out to explore the perceptions of Orthopaedic medicine students and their supervisors about practice-based learning at a tertiary training hospital.MethodsThis was an exploratory phenomenological qualitative study that involved in-depth interviews among 10 Orthopedic students during their rotation in the emergency ward of Mulago hospital and 6 of their supervisors. Interviews were audio-recorded, transcribed, and then imported into Atlas ti 8.3 for analysis. The data were coded and grouped into themes relating to perceptions of practice-based learning, general inductive analysis was used. The general inductive approach involved condensing the raw textual data into a brief and summary format. The summarized format was then analyzed to establish clear links between the perceptions of practice-based learning and the summary findings derived from the raw data.ResultsThe mean age of the students was 23 ± 1.5 years. Four out of the six supervisors were Orthopaedic officers while the remaining two were principal Orthopaedic officers, four out of the six had a university degree while the other two were diploma holders. The main themes arising were hands-on skills, an unconducive learning environment, the best form of learning, and having an undefined training structure. Particularly, the perceptions included the presence of too many students on the wards during the rotation, frequent stock-outs of supplies for learning, and supervisors being overwhelmed caring for a large number of patients.ConclusionBarriers to satisfactory practice-based learning were overcrowding on the wards and insufficient training materials. To improve practice-based learning, adequate learning materials are required and the number of students enrolled needs to be appropriate for the student – supervisor ratio.

Experiences of Community Members Engaged in eCPR (Emotional Connecting, Empowering, Revitalizing) Training: Qualitative Focus Group Study.

BackgroundThe United Nations has called for wide-scale community mental health psychoeducation; however, few programs currently exist. Emotional Connecting, Empowering, Revitalizing (eCPR) is a community education and training program developed by individuals with a lived experience of mental health challenges or trauma. It is designed to provide community members with skills and confidence to support someone experiencing mental health challenges.ObjectiveThis qualitative study aimed to examine the user experiences of diverse community members engaged in eCPR training. This study reviewed their attitudes toward training and opportunities for improvement in future implementations of training.MethodseCPR training participants (N=31) were invited to participate in virtual focus groups between June 2020 and July 2020. Data were analyzed using the rigorous and accelerated data reduction method, which converts raw textual data into concise data tables to develop a codebook, and thematic analysis was performed to identify common themes.ResultsThe themes identified when analyzing the data included emotional holding and containment, training feedback, principles and practices of eCPR, implementation, connection in a digital environment, skills practice, and shared experiences.ConclusionseCPR may benefit individuals from multiple, diverse demographics. It can enhance their ability to connect with others to understand what it means to be with someone who is experiencing a mental health challenge or crisis, to accept their own emotions, and to be confident in being their most authentic self in both their work and personal lives. eCPR may answer the call of the United Nations by bringing opportunities for authenticity and healing to community settings. Exploring the effects of delivering eCPR in communities on individuals experiencing distress is an important next step. This study found that eCPR may be beneficial to many groups of trainees with varying backgrounds and experiences. These findings are important, as they speak to the potential for eCPR to be implemented in a variety of community settings with the intention of working to improve mental health in everyday settings.

Equity returns and sentiment

Abstract This paper analyzes approximately 100 Gigabytes of raw text data from Twitter with keywords “AAPL,” “S&P 500,” “FTSE100” and “NASDAQ” to explore the relationship between sentiment and the returns and prices on the Apple stock and the S&P 500, FTSE 100 and NASDAQ indices. The findings point to significant relationship and dependence between sentiment measures and the S&P 500 and FTSE 100 indices’ returns and prices. The econometric analysis of dependence between the aforementioned variables in the paper is presented in some detail for illustration of the methodology employed.

Diffusion of Influence? Detecting China’s Footprint in Foreign Policies of Other Countries

“Chinese influence” has become a hot topic among politicians, media, and pundits. Academic studies dealing with the subject, however, generally take a much more cautious position. This paper suggests an innovative approach of studying political influence using data from the UN General Assembly voting and speech records. Utilizing the latest advancements in natural language processing (NLP), we show how to estimate, quantify, and visualise foreign policy positions using raw textual data. In Europe, it has been argued that China’s growing presence and activity in Central and Eastern European states (CEE) undermines “European unity” as the region allegedly starts shifting towards Beijing. We adopt the CEE region as a case study of China’s potential influence and highlight the positioning of CEE countries towards China, Germany, and the U.S. by visualizing the proximities among them over time. We find no evidence that the CEE countries are strategically pivoting towards China. We have controlled for known positions and trends of other countries from elsewhere in the world to strengthen the case for our methodological approach.Supplementary InformationThe online version contains supplementary material available at 10.1007/s41111-022-00217-5.

Table detection in business document images by message passing networks

Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches.