Natural Language Processing Methods Research Articles

Ontology extension with NLP-based concept extraction for domain experts in catalytic sciences

Ontologies store semantic knowledge in a machine-readable way and represent domain knowledge in controlled vocabulary. In this work, a workflow is set up to derive classes from a text dataset using natural language processing (NLP) methods. Furthermore, ontologies and thesauri are browsed for those classes and corresponding existing textual definitions are extracted. A base ontology is selected to be extended with knowledge from catalysis science, while word similarity is used to introduce new classes to the ontology based on the class candidates. Relations are introduced to automatically reference them to already existing classes in the selected ontology. The workflow is conducted for a text dataset related to catalysis research on methanation of CO_2 and seven semantic artifacts assisting ontology extension by domain experts. Undefined concepts and unstructured relations can be more easily introduced automatically into existing ontologies. Domain experts can then revise the resulting extended ontology by choosing the best fitting definition of a class and specifying suggested relations between concepts of catalyst research. A structured extension of ontologies supported by NLP methods is made possible to facilitate a Findable, Accessible, Interoperable, Reusable (FAIR) data management workflow.

HomeADScreen: Developing Alzheimer's disease and related dementia risk identification model in home healthcare

BackgroundMore than 50 % of patients with Alzheimer's disease and related dementia (ADRD) remain undiagnosed. This is specifically the case for home healthcare (HHC) patients. ObjectivesThis study aimed at developing HomeADScreen, an ADRD risk screening model built on the combination of HHC patients' structured data and information extracted from HHC clinical notes. MethodsThe study’s sample included 15,973 HHC patients with no diagnosis of ADRD and 8,901 patients diagnosed with ADRD across four follow-up time windows. First, we applied two natural language processing methods, Word2Vec and topic modeling methods, to extract ADRD risk factors from clinical notes. Next, we built the risk identification model on the combination of the Outcome and Assessment Information Set (OASIS-structured data collected in the HHC setting) and clinical notes-risk factors across the four-time windows. ResultsThe top-performing machine learning algorithm attained an Area under the Curve = 0.76 for a four-year risk prediction time window. After optimizing the cut-off value for screening patients with ADRD (cut-off-value = 0.31), we achieved sensitivity = 0.75 and an F1-score = 0.63. For the first-year time window, adding clinical note-derived risk factors to OASIS data improved the overall performance of the risk identification model by 60 %. We observed a similar trend of increasing the model's overall performance across other time windows. Variables associated with increased risk of ADRD were “hearing impairment” and “impaired patient ability in the use of telephone.” On the other hand, being “non-Hispanic White” and the “absence of impairment with prior daily functioning” were associated with a lower risk of ADRD. ConclusionHomeADScreen has a strong potential to be translated into clinical practice and assist HHC clinicians in assessing patients' cognitive function and referring them for further neurological assessment.

BOOK RECOMMENDATION SYSTEM USING PYTHON

An innovative book recommendation tool called BookWise was created to help readers get individualized book suggestions based on their unique preferences. Effective recommendation systems are now more necessary than ever to guide readers through the overwhelming amount of options given the exponential expansion of the digital book market. Advanced machine learning algorithms and methodsfor natural language processing are used by BookWise to analyze a variety of features of books, including genre, author, narrative, and style, as well as user information like reading habits, ratings, and history. These elements are combined by BookWise to produce precise recommendationsthat are pertinent to the preferences of each user. BookWise's ability to learn from and adjust to the evolving preferences of its users over time is at the heart of the program. The system uses collaborative filtering and content-based filtering approaches to continuously gather user feedback on suggested books and take user interactions into account. By doing this, it is able to make increasingly accurate and interesting recommendations to users. By implementing a user review and rating system, BookWise also encourages social involvement and a sense of community among its members. Users can communicate with people who share their interests in literature, discussreviews, and offertheirthoughts. This community-driven strategy improves the quality of suggestions while also enhancing the reading experience by encouraging interactions and relationships amongst book lovers. Furthermore, BookWise is created to be very user- friendly, giving cross-platform compatibility, including online and mobile applications, to make sure users can get their tailored recommendations wherever they are and wheneverthey want. The system also offers a simple and user-friendly design that makes it simple to navigate and explore books of all genres, new releases, and popular works. Through thorough user testing and review, BookWise's efficacy has been confirmed, proving its capacity to provide precise and pleasing recommendations that are in line with consssumers' interests. BookWise represents a significant advancement in the field of book recommendation systems by providing a potent tool to improve reader experiences, promote literary research in the digital age, and ease discovery. Keywords—Personalize book recommendation; recommendation system; clustering; machine learning

Using Health-Related Social Media to Understand the Experiences of Adults With Lung Cancer in the Era of Immuno-Oncology and Targeted Therapies: Observational Study.

The treatment of non-small cell lung cancer (NSCLC) has evolved dramatically with the approval of immuno-oncology (IO) and targeted therapies (TTs). Insights on the patient experience with these therapies and their impacts are lacking. Health-related social media has been increasingly used by patients to share their disease and treatment experiences, thus representing a valuable source of real-world data to understand the patient's voice and uncover potential unmet needs. This study aimed to describe the experiences of patients with NSCLC as reported in discussions posted on lung cancer-specific social media with respect to their disease symptoms and associated impacts. Publicly available posts (2010-2019) were extracted from selected lung cancer- or NSCLC-specific websites. Social media users (patients and caregivers posting on these websites) were stratified by metastatic- and adjuvant-eligible subgroups and treatment received using natural language processing (NLP) and machine learning methods. Automated identification of symptoms was conducted using NLP. Qualitative data analysis (QDA) was conducted on random samples of posts mentioning pain-related, fatigue-related, respiratory-related, or infection-related symptoms to capture the patient experience with these and associated impacts. Overall, 1724 users (50,390 posts) and 574 users (4531 posts) were included in the metastatic group and adjuvant group, respectively. Among users in the metastatic group, pain, discomfort, and fatigue were the most commonly mentioned symptoms (49.7% and 39.6%, respectively), and in the QDA (258 posts from 134 users), the most frequent impacts related to physical impairments, sleep, and eating habits. Among users in the adjuvant group, pain, discomfort, and respiratory symptoms were the most commonly mentioned (44.8% and 23.9%, respectively), and impacts identified in the QDA (154 posts from 92 users) were mostly related to physical functioning. Findings from this exploratory observational analysis of social media among patients and caregivers informed the lived experience of NSCLC in the era of novel therapies, shedding light on most reported symptoms and their impacts. These findings can be used to inform future research on NSCLC treatment development and patient management.

Profiling of patients with type 2 diabetes based on medication adherence data.

Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases. We use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering. Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40-50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55-75. Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

Differential Expression of Anomalous Self-Experiences in Spontaneous Speech in Clinical High-Risk and Early-Course Psychosis Quantified by Natural Language Processing

BackgroundBasic self-disturbance, or anomalous self-experiences (ASEs), is a core feature of the schizophrenia spectrum. We propose a novel method of natural language processing to quantify ASEs in spoken language by direct comparison to an inventory of self-disturbance, the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE). We hypothesized that there would be increased similarity in open-ended speech to the IPASE items in individuals with early-course psychosis (PSY) compared with healthy individuals, with clinical high-risk (CHR) individuals intermediate in similarity. MethodsOpen-ended interviews were obtained from 170 healthy control participants, 167 CHR participants, and 89 PSY participants. We calculated the semantic similarity between IPASE items and “I” sentences from transcribed speech samples using S-BERT (Sentence Bidirectional Encoder Representation from Text). Kolmogorov-Smirnov tests were used to compare distributions across groups. A nonnegative matrix factorization of cosine similarity was performed to rank IPASE items. ResultsSpoken language of CHR individuals had the greatest semantic similarity to IPASE items when compared to both healthy control (s = 0.44, p < 10−14) and PSY (s = 0.36, p < 10−6) individuals, while IPASE scores were higher among PSY than CHR group participants. In addition, the nonnegative matrix factorization approach produced a data-driven domain that differentiated the CHR group from the others. ConclusionsWe found that open-ended interviews elicited language with increased semantic similarity to the IPASE by participants in the CHR group compared with patients with psychosis. This demonstrates the utility of these methods for differentiating patients from healthy control participants. This complementary approach has the capacity to scale to large studies investigating phenomenological features of schizophrenia and potentially other clinical populations.

Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

Introduction: PD-L1 expression is used to determine oncology patients' response to and eligibility for immunologic treatments; however, PD-L1 expression status often only exists in unstructured clinical notes, limiting ability to use it in population-level studies. Methods: We developed and evaluated a machine learning based natural language processing (NLP) tool to extract PD-L1 expression values from the nationwide Veterans Affairs electronic health record system. Results: The model demonstrated strong evaluation performance across multiple levels of label granularity. Mean precision of the overall PD-L1 positive label was 0.859 (sd, 0.039), recall 0.994 (sd, 0.013), and F1 0.921 (0.024). When a numeric PD-L1 value was identified, the mean absolute error of the value was 0.537 on a scale of 0 to 100. Conclusion: We presented an accurate NLP method for deriving PD-L1 status from clinical notes. By reducing the time and manual effort needed to review medical records, our work will enable future population-level studies in cancer immunotherapy.

Analysis of User-generated Content in Visitor Reviews of Tourist Attractions Using Semantic Similarity

The tourism industry plays an important role in the world economic sector because it makes a significant contribution to the global economy, creates jobs, and strengthens economic growth. Therefore, visitor satisfaction is very important in this industry. Currently tourists use information in online media to find tourist attractions that suit their needs and expectations. Reviews of tourist attractions are growing on the internet. Customers can post reviews, recommendations, or ratings of a tourist spot. Online reviews in the form of User-generated Content (UGC) can provide benefits for business managers to obtain feedback from customers and improve certain product attributes or service characteristics in order to increase business value and support marketing activities. In this study, data collection will be carried out from one of the UGC tourism data sources, namely visitor reviews on Google Maps. Data analysis was performed using the natural language processing method. Analysis of these data will produce comprehensive information about tourist feedback.

Real-time social media sentiment analysis for rapid impact assessment of floods

Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions.Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time.In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time.

Classification of Patients' Judgments of Their Physicians in Web-Based Written Reviews Using Natural Language Processing: Algorithm Development and Validation.

Patients increasingly rely on web-based physician reviews to choose a physician and share their experiences. However, the unstructured text of these written reviews presents a challenge for researchers seeking to make inferences about patients' judgments. Methods previously used to identify patient judgments within reviews, such as hand-coding and dictionary-based approaches, have posed limitations to sample size and classification accuracy. Advanced natural language processing methods can help overcome these limitations and promote further analysis of physician reviews on these popular platforms. This study aims to train, test, and validate an advanced natural language processing algorithm for classifying the presence and valence of 2 dimensions of patient judgments in web-based physician reviews: interpersonal manner and technical competence. We sampled 345,053 reviews for 167,150 physicians across the United States from Healthgrades.com, a commercial web-based physician rating and review website. We hand-coded 2000 written reviews and used those reviews to train and test a transformer classification algorithm called the Robustly Optimized BERT (Bidirectional Encoder Representations from Transformers) Pretraining Approach (RoBERTa). The 2 fine-tuned models coded the reviews for the presence and positive or negative valence of patients' interpersonal manner or technical competence judgments of their physicians. We evaluated the performance of the 2 models against 200 hand-coded reviews and validated the models using the full sample of 345,053 RoBERTa-coded reviews. The interpersonal manner model was 90% accurate with precision of 0.89, recall of 0.90, and weighted F1-score of 0.89. The technical competence model was 90% accurate with precision of 0.91, recall of 0.90, and weighted F1-score of 0.90. Positive-valence judgments were associated with higher review star ratings whereas negative-valence judgments were associated with lower star ratings. Analysis of the data by review rating and physician gender corresponded with findings in prior literature. Our 2 classification models coded interpersonal manner and technical competence judgments with high precision, recall, and accuracy. These models were validated using review star ratings and results from previous research. RoBERTa can accurately classify unstructured, web-based review text at scale. Future work could explore the use of this algorithm with other textual data, such as social media posts and electronic health records.

Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach.

Pain is a widespread issue, with 20% of adults (1 in 5) experiencing it globally. A strong association has been demonstrated between pain and mental health conditions, and this association is known to exacerbate disability and impairment. Pain is also known to be strongly related to emotions, which can lead to damaging consequences. As pain is a common reason for people to access health care facilities, electronic health records (EHRs) are a potential source of information on this pain. Mental health EHRs could be particularly beneficial since they can show the overlap of pain with mental health. Most mental health EHRs contain the majority of their information within the free-text sections of the records. However, it is challenging to extract information from free text. Natural language processing (NLP) methods are therefore required to extract this information from the text. This research describes the development of a corpus of manually labeled mentions of pain and pain-related entities from the documents of a mental health EHR database, for use in the development and evaluation of future NLP methods. The EHR database used, Clinical Record Interactive Search, consists of anonymized patient records from The South London and Maudsley National Health Service Foundation Trust in the United Kingdom. The corpus was developed through a process of manual annotation where pain mentions were marked as relevant (ie, referring to physical pain afflicting the patient), negated (ie, indicating absence of pain), or not relevant (ie, referring to pain affecting someone other than the patient, or metaphorical and hypothetical mentions). Relevant mentions were also annotated with additional attributes such as anatomical location affected by pain, pain character, and pain management measures, if mentioned. A total of 5644 annotations were collected from 1985 documents (723 patients). Over 70% (n=4028) of the mentions found within the documents were annotated as relevant, and about half of these mentions also included the anatomical location affected by the pain. The most common pain character was chronic pain, and the most commonly mentioned anatomical location was the chest. Most annotations (n=1857, 33%) were from patients who had a primary diagnosis of mood disorders (International Classification of Diseases-10th edition, chapter F30-39). This research has helped better understand how pain is mentioned within the context of mental health EHRs and provided insight into the kind of information that is typically mentioned around pain in such a data source. In future work, the extracted information will be used to develop and evaluate a machine learning-based NLP application to automatically extract relevant pain information from EHR databases.

IMPROVING ONLINE MEETING EFFICIENCY USING LATENT DIRICHLET ALLOCATION (LDA) AND SOCIAL NETWORK ANALYSIS (SNA) METHODS

The pandemic period can change the habits of a person and organization, where all meetings are not held face-to-face/offline but virtually, so it is not uncommon for meetings to be attended by employees who are not Persons in Charge (PIC) on certain meeting topics. This study aims to identify trends in time, day, and duration of meetings within the Secretariat General of the Ministry of Finance and to cluster meeting matters into several themes so that further identification can be carried out to provide recommendations for units having duties related to the meeting using networking analysis. This study uses the Natural Language Processing (NLP) method with Latent Dirichlet Allocation (LDA) which can conclude the factors that represent topics to produce topic clustering and Social Network Analysis (SNA) modeling using the Degree Centrality method to find out the closest relationship between topics and names. unit based on the highest centrality value and the possibility of a unit attending a meeting that discusses a particular topic. Data used in this reseacrh are meetings held during April 2020 up to April 2022 with 59,891 data records. The modeling results shows clustering result dashboard based on meeting topics and to produce an analysis of which meeting topics are often discussed and become a concern. The results of the research are expected to be used to provide recommendations to unit leaders to assign meeting dispositions for each PIC to attend the meeting.

Authorship Identification of Binary and Disassembled Codes Using NLP Methods

This article is part of a series aimed at determining the authorship of source codes. Analyzing binary code is a crucial aspect of cybersecurity, software development, and computer forensics, particularly in identifying malware authors. Any program is machine code, which can be disassembled using specialized tools and analyzed for authorship identification, similar to natural language text using Natural Language Processing methods. We propose an ensemble of fastText, support vector machine (SVM), and the authors’ hybrid neural network developed in previous works in this research. The improved methodology was evaluated using a dataset of source codes written in C and C++ languages collected from GitHub and Google Code Jam. The collected source codes were compiled into executable programs and then disassembled using reverse engineering tools. The average accuracy of author identification for disassembled codes using the improved methodology exceeds 0.90. Additionally, the methodology was tested on the source codes, achieving an average accuracy of 0.96 in simple cases and over 0.85 in complex cases. These results validate the effectiveness of the developed methodology and its applicability to solving cybersecurity challenges.

Mental health at different stages of cancer survival: a natural language processing study of Reddit posts.

The purpose of this study was to use text-based social media content analysis from cancer-specific subreddits to evaluate depression and anxiety-loaded content. Natural language processing, automatic, and lexicon-based methods were employed to perform sentiment analysis and identify depression and anxiety-loaded content. Data was collected from 187 Reddit users who had received a cancer diagnosis, were currently undergoing treatment, or had completed treatment. Participants were split according to survivorship status into short-term, transition, and long-term cancer survivors. A total of 72524 posts were analyzed across the three cancer survivor groups. The results showed that short-term cancer survivors had significantly more depression-loaded posts and more anxiety-loaded words than long-term survivors, with no significant differences relative to the transition period. The topic analysis showed that long-term survivors, more than other stages of survivorship, have resources to share their experiences with suicidal ideation and mental health issues while providing support to their survivor community. The results indicate that Reddit texts seem to be an indicator of when the stressor is active and mental health issues are triggered. This sets the stage for Reddit to become a platform for screening and first-hand intervention delivery. Special attention should be dedicated to short-term survivors.

A topic-aware classifier based on a hybrid quantum-classical model

In the era of Large Language Models, there is still potential for improvement in current Natural Language Processing (NLP) methods in terms of verifiability and consistency. NLP classical approaches are computationally expensive due to their high-power consumption, computing power, and storage requirements. Another computationally efficient approach to NLP is categorical quantum mechanics, which combines grammatical structure and individual word meaning to deduce the sentence meaning. As both quantum theory and natural language use vector space to describe states which are more efficient on quantum hardware, QNLP models can achieve up to quadratic speedup over classical direct calculation methods. In recent years, there is significant progress in utilizing quantum features such as superposition and entanglement to represent linguistic meaning on quantum hardware. Earlier research work has already demonstrated QNLP’s potential quantum advantage in terms of speeding up search, enhancing classification tasks’ accuracy and providing an exponentially large quantum state space in which complex linguistic structures can be efficiently embedded. In this work, a QNLP model is used to determine if two sentences are related to the same topic or not. By comparing our QNLP model to a classical tensor network-based one, our model improved training accuracy by up to 45% and validation accuracy by 35%, respectively. The QNLP model convergence is also studied when varying: first, the problem size, second, parametrized quantum circuits used for model’s training, and last, the backend quantum simulator noise model. The experimental results show that strongly entangled ansatz designs result in fastest model convergence.

1287-P: Identifying When Incident Diabetes Was Diagnosed in Children and Young Adults, Using Natural Language Processing of Clinical Notes

Identifying when an incident diabetes (DM) diagnosis was made is complicated using retrospective, structured electronic health record (EHR) data alone. Unstructured clinical notes have been underused but contain valuable information that could complement traditional methods. However, manually reviewing clinical notes is time-consuming. We developed and validated a simple rule-based Natural Language Processing (NLP) method to extract incident DM timing from clinical notes. In a single center we used structured EHR data to identify a cohort (age &amp;lt;45 as of 12/31/19) with likely type 1 (T1D) or type 2 (T2D) DM based on 2016-2019 records: (≥1 T1D ICD-10 code and insulin and no other DM medication) or ([≥2 T2D and no T1D codes] or [≥1 T2D code and a DM medication besides insulin or metformin]). This cohort had 2,654 patients (548,316 clinical notes, 2003-present). We randomly selected 58,450 clinical notes (1,465 patients) as a training set to look for relevant text patterns. We handcrafted the rules into our NLP tool. We required 3 distinct concepts at the sentence level to determine an incident DM diagnosis: DM (not, e.g., epilepsy), an onset attribute (e.g., “diagnosed in”), and a temporal component (e.g., 8/2008). We pre-defined all related keywords and date formats for these concepts in our training notes. We then tested the NLP algorithm against manual review in an independent set of 100 randomly selected patients from the cohort. Analysis was at the patient level (true+: ≥1 true+ note per patient). NLP in the training set found 1,268 patients with at least 1 of the 3 concepts and 826 patients with all 3. In the test set, we excluded 4 patients without substantive notes. NLP correctly detected incident DM timing in 73 of 96 patients. The NLP had recall 88%, specificity 77%, precision (PPV) 96%, and NPV 50%. NLP was helpful in finding incident DM timing and may complement structured EHR queries for identifying incident DM. Refinement of our NLP algorithm is ongoing. Disclosure A.Wong: None. V.W.Zhong: None. M.Rosenman: None. Funding Centers for Disease Control and Prevention (1U18DP006693-01-00)

Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users

Background: Suicide risk prediction models frequently rely on structured electronic health record (EHR) data, including patient demographics and health care usage variables. Unstructured EHR data, such as clinical notes, may improve predictive accuracy by allowing access to detailed information that does not exist in structured data fields. To assess comparative benefits of including unstructured data, we developed a large case-control dataset matched on a state-of-the-art structured EHR suicide risk algorithm, utilized natural language processing (NLP) to derive a clinical note predictive model, and evaluated to what extent this model provided predictive accuracy over and above existing predictive thresholds.Methods: We developed a matched case-control sample of Veterans Health Administration (VHA) patients in 2017 and 2018. Each case (all patients that died by suicide in that interval, n = 4,584) was matched with 5 controls (patients who remained alive during treatment year) who shared the same suicide risk percentile. All sample EHR notes were selected and abstracted using NLP methods. We applied machine-learning classification algorithms to NLP output to develop predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy overall and for high-risk patients.Results: The best performing NLP-derived models provided 19% overall additional predictive accuracy (AUC = 0.69; 95% CI, 0.67, 0.72) and 6-fold additional risk concentration for patients at the highest risk tier (top 0.1%), relative to the structured EHR model.Conclusions: The NLP-supplemented predictive models provided considerable benefit when compared to conventional structured EHR models. Results support future structured and unstructured EHR risk model integrations.

AUTOMATED CONDITION DETECTION IN REQUIREMENTS ENGINEERING

AbstractIn product development, it is of great importance that a complete, unambiguous, and, as far as possible, contradiction-free target system is defined. Requirements documents of complex systems can contain several thousand individual requirements, derived in an interdisciplinary manner and written in natural language by many different stakeholders. Hence, errors, in the form of contradictions, cannot be completely avoided in these documents and today they must be corrected manually with high effort.This paper presents an important building block for automated contradiction detection and quality analysis of requirements documents. We discuss the necessary identification of conditions in requirements and the extraction of the verbal expressions associated with condition and effect, respectively. We applied and analyzed natural language processing methods based on grammatical versus machine learning models. The models have been applied to 1,861 real-world requirements. Both approaches generate promising results, with an accuracy partly over 98%. However, in structured specification texts, a grammatical model is preferable due to lower effort in preprocessing and better usability.

Uncovering Cyberbullying Themes from Unconsented Facebook Pitik Post Through Text Mining Techniques

Pitik, referred as street photography; is a colloquial phrase for taking random photographs, arbitrarily from people without permission and/or consent. Empirically, numerous comments circulating in Facebook Pitik posts containing words related to humiliation, embarrassment, shaming, stalking, and bullying. There are yet no studies conducted to confirm or prove the existence and extent of cyberbullying themes in Facebook Pitik trends. Cyberbullying is not new to social media environments and as technologies and trends change over time, the medium of cyberbullying also changes. We show in this study the proofs of the existence and the extents of cyber bullying themes in Facebook Pitik posts. In this study, we utilized methods of natural language processing – specifically text mining and emotion polarity computation, also known as sentiment analysis. Using Facepager software, 68,000 documents/comments are collected from select Facebook pages of photographers involved in the trend of Pitik posting. Results showed that the collected documents contain 26.29 % pertaining to harassment; 35.48% to flaming; and 19.45% to denigration. The existence of negative emotions is also seen from collected documents including anger, uncertainty, constraining, fear, sadness, and disgust. Findings may help policy makers to enhance the Facebook community standards making its app safer and free from issues relating to cyberbullying, especially in unconsented Pitik posts.

Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features

The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recognition on the unannotated statements. However, due to the uncertainty of NLP (natural language processing) in short scenes and the complexity of Chinese text, existing methods have some limitations in accuracy and application scenarios. In this paper, we propose an opinion tendency recognition model HGAT (heterogeneous graph attention network) that integrates text vector and context structure methods to address the above problems. This method first trains a text vectorization model based on annotation text content, then constructs an isomorphic graph with annotation, news, and theme as its apex, and then optimizes the feature vectors of all nodes using an isomorphic graph neural network model with attention mechanism. In addition, this article collected 1,684,318 news items and 57,845,091 comments based on Toutiao, sifted through 511 of those stories and their corresponding 103,787 comments, and tested the impact of HGAT on this dataset. Experiments show that this method has stable improvement effect on different NLP methods, increasing accuracy by 2–10%, and provides a new perspective for opinion tendency recognition.