Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

With the trend of globalization and digitalization, many transnational companies are continuously collecting and storing unstructured text data in different languages. To exploit the business value of such high-volume multilingual text data, cross-lingual information extraction utilizes machine translation and other natural language processing (NLP) techniques to analyze this data. However, results of these analysis heavily depend on the order in which the tasks are performed as well as the used machine translation and NLP approaches or trained models. In this paper, we defined and evaluated a series of cross-lingual text processing pipelines for English and Chinese language. We therefore combine multiple commercial machine translation services with different automatic keyphrase extraction and named entity recognition techniques and evaluate their performance with regards to the order of execution. Hence, we evaluate the combination of machine translation systems and natural language processing techniques with two processing sequences in our experiment. One is to translate the document before extracting keyphrase and named entities. The other is to translate the processing results. The experiment outcomes indicate that translating documents is a better choice than the other way around in both tasks. However, there exists a substantial disparity between the performance of the cross-lingual text processing pipelines and the corresponding monolingual references.

Social media, a buzz term in the modern world, refers to various online platforms like social networks, forums, blogs and blog comments, microblogs, wikis, media sharing platforms, social bookmarks through which communication between individuals, communities, or groups takes place. People over social media do not only share their ideas and opinions, but it has become an important source through which businesses promote their products. Analyzing huge data generated over social media is useful in various tasks like analyzing customer trends, forecast sales, understanding opinions of people on different hot topics, views of customers about services/products, and many more. Different natural language processing (NLP) techniques are used for crawling and processing social media data to get useful insights out of this. In this chapter, the focus is on various NLP techniques used to process the social media data. Challenges faced by NLP techniques to process social media data are also put forward in this chapter.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises such as the COVID-19 pandemic. This study aimed to analyze SU trends at the user level across different demographic dimensions, such as age, gender, race, and ethnicity, with a focus on the COVID-19 pandemic. The study also establishes a baseline for SU trends using social media data. The study was conducted using large-scale English-language data from Twitter (now known as X) over a 3-year period (2019, 2020, and 2021), comprising 1.13 billion posts. Following preprocessing, the SU posts were identified using our custom-trained deep learning model (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach [RoBERTa]), which resulted in the identification of 9 million SU posts. Then, demographic attributes, such as user type, age, gender, race, and ethnicity, as well as sentiments and emotions associated with each post, were extracted via a collection of natural language processing modules. Finally, various qualitative analyses were performed to obtain insight into user behaviors based on demographics. The highest level of user participation in SU discussions was observed in 2020, with a 22.18% increase compared to 2019 and a 25.24% increase compared to 2021. Throughout the study period, male users and teenagers increasingly dominated the SU discussions across all substance types. During the COVID-19 pandemic, user participation in prescription medication discussions was notably higher among female users compared to other substance types. In addition, alcohol use increased by 80% within 2 weeks after the global pandemic declaration in 2020. This study presents a large-scale, fine-grained analysis of SU on social media data, examining trends by age, gender, race, and ethnicity before, during, and after the COVID-19 pandemic. Our findings, contextualized with sociocultural and pandemic-specific factors, provide actionable insights for targeted public health interventions. This study establishes social media data (powered with artificial intelligence and natural language processing tools) as a valuable platform for real-time SU surveillance and prevention during crises.

Mining social media data to assess the risk of skin and soft tissue infections from allergen immunotherapy

Task-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. while existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge.KeywordsTask-oriented conversational botsComplex intent recognitionContext knowledgeSlot value inference

In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice. What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

The AI voice assistant mobile application was developed to aid drivers in operating their mobile phones while driving without touching their phones. The literature review examines multiple innovative artificial technologies involved in applications with voice assistants in natural language processing (NLP) techniques. The methodology used involved a qualitative approach, and the design science paradigm was used for the development of the voice assistant for smartphones with NLP techniques. NLP techniques that were applied in the development of the AI voice assistant are smart synthesis, data flow sequence, core and interface accessing, part of speech tagging, named entity recognition, conference resolution, and porter stemming. Some of the operations that are achieved by the application include arithmetic calculations based on voice commands and returning the computer result via voice, searching the internet based on user voice input, and providing a response via voice assistance.

IntroductionSince the Coronavirus disease 2019 (COVID-19), public safety measures, including social distancing and school closures, have been implemented, precipitating psychological difficulties and heightened online activities for adolescents. However, studies examining the impact of the pandemic on adolescent mental health and their coping strategies in Asian countries are limited. Further, most studies have used survey measures to capture mental health challenges so far. Accordingly, this study aimed to examine the psychological challenges South Korean adolescents experienced and their coping strategies during the pandemic using the Natural Language Processing (NLP) and Text mining (TM) technique on adolescents’ social media texts/posts.MethodsThe data were gathered from social media texts/posts such as online communities, Twitter, and personal blogs from January 1, 2019, to October 31, 2021. The 12,520,250 texts containing keywords related to adolescents’ common psychological difficulties reported during the pandemic, including self-harm, Attention-Deficit/Hyperactivity Disorders (ADHD), and insomnia, were analyzed by TM, NLP using information extraction, co-occurrence and sentiment analysis. The monthly frequency of the keywords and their associated words was also analyzed to understand the time trend.ResultsAdolescents used the word “self-harm” in their social media texts more frequently during the second wave of COVID-19 (August to September 2020). “Friends” was the most associated word with “self-harm.” While the frequency of texts with “Insomnia” stayed constant throughout the pandemic, the word “ADHD” was increasingly mentioned in social media. ADHD and insomnia were most frequently associated with ADHD medications and sleeping pills, respectively. Friends were generally associated with positive words, while parents were associated with negative words.ConclusionDuring COVID-19, Korean adolescents often expressed their psychological challenges on social media platforms. However, their coping strategies seemed less efficient to help with their difficulties, warranting strategies to support them in the prolonged pandemic era. For example, Korean adolescents shared psychological challenges such as self-harm with friends rather than their parents. They considered using medicine (e.g., sleeping pills and ADHD medication) as coping strategies for sleep and attention problems.