Composable Text Controls in Latent Space with ODEs

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

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.

Psychedelics and meditation are known for their potential to induce personally meaningful and even transformative experiences. However, it is unclear how similar these experiences are, or how they differ from each other. This explorative study used natural language processing (NLP) methods to compare reports of personally meaningful subjective experiences facilitated by either psychedelic substances or meditation. Participants (N = 197) wrote open-ended narrative reports about their most meaningful experience facilitated either with psychedelics (n = 134) or meditation (n = 63). These reports were analysed with text similarity analyses, topic modeling and sentiment analysis. The semantic and lexical contents of the reports were highly similar and both groups expressed positive emotions on average. However, psychedelic experience reports were more emotionally charged, showing higher levels of positive and negative sentiments compared to more neutral meditation experiences. These results suggest that the two types of subjective experiences might be quite similar in general, but emotional intensity could be a distinguishing factor between them. Challenges with the NLP methods and the dataset limit the conclusions that can be drawn from the study. However, it offers new hypotheses and suggestions for future research on transformative experiences.

The authors tested whether natural language processing (NLP) methods can detect and classify cognitive distortions in text messages between clinicians and people with serious mental illness as effectively as clinically trained human raters. Text messages (N=7,354) were collected from 39 clients in a randomized controlled trial of a 12-week texting intervention. Clinical annotators labeled messages for common cognitive distortions: mental filtering, jumping to conclusions, catastrophizing, "should" statements, and overgeneralizing. Multiple NLP classification methods were applied to the same messages, and performance was compared. A tuned model that used bidirectional encoder representations from transformers (F1=0.62) achieved performance comparable to that of clinical raters in classifying texts with any distortion (F1=0.63) and superior to that of other models. NLP methods can be used to effectively detect and classify cognitive distortions in text exchanges, and they have the potential to inform scalable automated tools for clinical support during message-based care for people with serious mental illness.

Objective - This paper focuses on analyzing the significance of sports footwear brands by processing large text data from the Internet. In a modern environment, the brand distinguishes a company's products or services from those of its competitors. A strong brand can help build trust with customers as they perceive the brand as reliable and trustworthy. Methodology/Technique - The study uses NLP (Natural Language Processing) methods to analyze rich text content on the Internet. The research focus is based on the application of innovative methods to determine the importance and value of a brand using NLP techniques by analyzing the content of a large corpus of text originating from websites dealing with sports footwear brands. The NLP analysis models were programmed using the low-code analysis tool KNIME. Findings – The analysis is carried out for the most well-known sports footwear brands such as Nike, Adidas, Puma, Under Armour, Reebok and Asics. The research object refers to the analysis of brand significance and the evaluation of consumer opinions on sports footwear, based on the processing of large text data from the internet. Novelty - The research results are based on an innovative approach to measuring and evaluating the brand significance in sports footwear using NLP methods to analyze large text content from the Internet. The results obtained show that this new approach to metrics and evaluation can significantly improve existing methods of brand evaluation. Type of Paper: Empirical JEL Classification: M32, M39. Keywords: Brand, NLP Method, Text Analysis, Online Brand Management Strategies, Sports Footwear. Reference to this paper should be made as follows: Tarade, S.S; Vuković, D. (2023). Discovering the Significance of Sports Footwear Brands through Text Analysis, J. Mgt. Mkt. Review, 8(4), 166 – 175. https://doi.org/10.35609/jmmr.2023.8.4(7)

Cyberbullying has been a significant issue in online communities, posing threats to individuals' mental and social health. This paper examines the use of deep learning and natural language processing (NLP) methods for automated cyberbullying detection in text data. A complete preprocessing pipeline is applied, involving URL and mention removal, stop-word removal, and text normalization, to improve data quality for model training. For the detection issue, various architectures of deep learning are used, i.e., Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and hybrid models like CNN-LSTM and CNN-GRU. Tokenization and padding on the dataset ensure equal input length before training on the model. Evaluation on performance is done on accuracy, precision, recall, and F1-score with comparative analysis on all models. Experimental evidence reveals that hybrid models are more accurate than independent CNN and RNN architectures, with better classification accuracy and generalization. This highlights the potential of combining deep learning and NLP methods in building automated systems for cyberbullying identification. This work's findings can inform the development of AI-powered content moderation tools, leading to safer, more inclusive, and robust digital spaces.

In recent years, as people’s awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers’ perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of “Technology Maturity” (A13) which reached 25,329, and which belongs to the “Performance Risk” (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the “Social Risk” (PR5) dimension, the abstract “Social Needs” (A51) received only 3224 comments and “Preference and Trust Rank” (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension’s emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the “Time Risk” (PR4), accounting for 54%. On a finer scale, the top three negative emotions are “Charging Time” (A42), “EV Charging Facilities” (A41), and “Maintenance of Value” (A33). Another interesting result is that “Social Needs” (A51)’s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers’ perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs’ consumption market in China.

Underreporting of privacy-laden family financial exploitation of older adults and Federal underinvestment in response to elder mistreatment can adversely influence older people’s help-seeking, health, and mental health outcomes. This study compared public attitudes toward family financial exploitation by focusing on one victim (21 YouTube videos), one perpetrator (8), and a case of family financial abuse generally (19), totaling 3,796 comments included in the analysis. The victims and perpetrators in the videos were not related. Three levels of analysis were conducted: sentiment and emotion analyses using advanced natural language processing (NLP) methods and a human-coded subsample. Sentiment analysis showed that among the three cases, a greater share of negative (about 40%) and positive (nearly 60%) comments was attributed to the perpetrator and victim, respectively. Emotion analysis found no shared emotions between the victim and perpetrator cases. Disapproval, surprise, disgust, and confusion were expressed solely concerning the perpetrator, whereas anger and fear were expressed solely concerning the victim. Caring, love, and gratitude were shared emotions across the victim and the general cases. Sadness, admiration, curiosity, annoyance, and approval were five emotions shared across all three cases. Human-coded sub-sample analysis showed expressions of sarcasm, and microaggressions directed at the perpetrator’s race and gender. Although NLP methods did not capture victim-perpetrator background-related attitudes in relation to family financial abuse, they delineated relatively clear-cut attitudes toward victim and perpetrator status, reflecting a public concern toward elder family financial exploitation. Public attitudes may be harnessed for stakeholder engagement to increase reporting and investment in elder mistreatment.

Shengli Sun, Qingfeng Sun, Kevin Zhou, Tengchao Lv. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

David Lowell, Zachary C. Lipton, Byron C. Wallace. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Hugh Perkins, Yi Yang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Kazuma Kadowaki, Ryu Iida, Kentaro Torisawa, Jong-Hoon Oh, Julien Kloetzer. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Xinchi Chen, Chunchuan Lyu, Ivan Titov. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Min Zeng, Yisen Wang, Yuan Luo. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.