Natural Language Processing Journal最新文献

{"title":"Emotion on the edge: An evaluation of feature representations and machine learning models","authors":"James Thomas Black,&nbsp;Muhammad Zeeshan Shakir","doi":"10.1016/j.nlp.2025.100127","DOIUrl":"10.1016/j.nlp.2025.100127","url":null,"abstract":"<div><div>This paper presents a comprehensive analysis of textual emotion classification, employing a tweet-based dataset to classify emotions such as surprise, love, fear, anger, sadness, and joy. We compare the performances of nine distinct machine learning classification models using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) feature representations, as well as a fine-tuned DistilBERT transformer model. We examine the training and inference times of models to determine the most efficient combination when employing an edge architecture, investigating each model’s performance from training to inference using an edge board. The study underscores the significance of combinations of models and features in machine learning, detailing how these choices affect model performance when low computation power needs to be considered. The findings reveal that feature representations significantly influence model efficacy, with BoW and TF-IDF models outperforming DistilBERT. The results show that while BoW models tend to have higher accuracy, the overall performance of TF-IDF models is superior, requiring less time for fitting, Stochastic Gradient Descent and Support Vector Machines proving to be the most efficient in terms of performance and inference times.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"10 ","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RESPECT: A framework for promoting inclusive and respectful conversations in online communications","authors":"Shaina Raza ,&nbsp;Abdullah Y. Muaad ,&nbsp;Emrul Hasan ,&nbsp;Muskan Garg ,&nbsp;Zainab Al-Zanbouri ,&nbsp;Syed Raza Bashir","doi":"10.1016/j.nlp.2025.100126","DOIUrl":"10.1016/j.nlp.2025.100126","url":null,"abstract":"<div><div>Toxicity and bias in online conversations hinder respectful interactions, leading to issues such as harassment and discrimination. While advancements in natural language processing (NLP) have improved the detection and mitigation of toxicity on digital platforms, the evolving nature of social media conversations demands continuous innovation. Previous efforts have made strides in identifying and reducing toxicity; however, a unified and adaptable framework for managing toxic content across diverse online discourse remains essential. This paper introduces a comprehensive framework <strong>R</strong><span>ESPECT</span> designed to effectively identify and mitigate toxicity in online conversations. The framework comprises two components: an encoder-only model for detecting toxicity and a decoder-only model for generating debiased versions of the text. By leveraging the capabilities of transformer-based models, toxicity is addressed as a binary classification problem. Subsequently, open-source and proprietary large language models are utilized through prompt-based approaches to rewrite toxic text into non-toxic, and making sure these are contextually accurate alternatives. Empirical results demonstrate that this approach significantly reduces toxicity across various conversational styles, fostering safer and more respectful communication in online environments.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"10 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment analysis for stock market research: A bibliometric study","authors":"Xieling Chen ,&nbsp;Haoran Xie ,&nbsp;Zongxi Li ,&nbsp;Han Zhang ,&nbsp;Xiaohui Tao ,&nbsp;Fu Lee Wang","doi":"10.1016/j.nlp.2025.100125","DOIUrl":"10.1016/j.nlp.2025.100125","url":null,"abstract":"<div><div>Sentiment analysis is widely utilized in stock market research. To comprehensively review the field, a bibliometric analysis was performed on 223 articles relating to sentiment analysis for stock markets from 2010 to 2022 collected from Web of Science database. Specifically, we recognized active affiliations, countries/regions, publication sources, and subject areas, identified top cited research articles, visualized scientific collaborations among authors, affiliations, and countries/regions, and revealed main research topics. Findings indicate that computer science journals are active in publishing works on sentiment analysis-facilitated stock market research. The research on sentiment analysis-facilitated stock market has attracted researchers from a wide geographic distribution, who have made significant contributions. The intensity of intra-regional collaborations is higher than that of inter-regional collaborations. Thematic topics regarding stock market research using sentiment analysis were detected using keyword mapping, with the following research topics being widely concerned by scholars: deep learning for stock market prediction, financial news sentiment empowered stock trend forecasting, effects of investor sentiment on financial market, and microblog sentiment classification for market prediction. Findings are helpful in depicting research status to researchers and practitioners, raising their awareness of research frontiers when planning research projects concerning sentiment analysis’s application in stock markets.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"10 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of open and closed-source LLMs for low-resource language with zero-shot, few-shot, and chain-of-thought prompting","authors":"Zabir Al Nazi ,&nbsp;Md. Rajib Hossain ,&nbsp;Faisal Al Mamun","doi":"10.1016/j.nlp.2024.100124","DOIUrl":"10.1016/j.nlp.2024.100124","url":null,"abstract":"<div><div>As the global deployment of Large Language Models (LLMs) increases, the demand for multilingual capabilities becomes more crucial. While many LLMs excel in real-time applications for high-resource languages, few are tailored specifically for low-resource languages. The limited availability of text corpora for low-resource languages, coupled with their minimal utilization during LLM training, hampers the models’ ability to perform effectively in real-time applications. Additionally, evaluations of LLMs are significantly less extensive for low-resource languages. This study offers a comprehensive evaluation of both open-source and closed-source multilingual LLMs focused on low-resource language like Bengali, a language that remains notably underrepresented in computational linguistics. Despite the limited number of pre-trained models exclusively on Bengali, we assess the performance of six prominent LLMs, i.e., three closed-source (GPT-3.5, GPT-4o, Gemini) and three open-source (Aya 101, BLOOM, LLaMA) across key natural language processing (NLP) tasks, including text classification, sentiment analysis, summarization, and question answering. These tasks were evaluated using three prompting techniques: Zero-Shot, Few-Shot, and Chain-of-Thought (CoT). This study found that the default hyperparameters of these pre-trained models, such as temperature, maximum token limit, and the number of few-shot examples, did not yield optimal outcomes and led to hallucination issues in many instances. To address these challenges, ablation studies were conducted on key hyperparameters, particularly temperature and the number of shots, to optimize Few-Shot learning and enhance model performance. The focus of this research is on understanding how these LLMs adapt to low-resource downstream tasks, emphasizing their linguistic flexibility and contextual understanding. Experimental results demonstrated that the closed-source GPT-4o model, utilizing Few-Shot learning and Chain-of-Thought prompting, achieved the highest performance across multiple tasks: an F1 score of 84.54% for text classification, 99.00% for sentiment analysis, a <span><math><mrow><mi>F</mi><msub><mrow><mn>1</mn></mrow><mrow><mi>b</mi><mi>e</mi><mi>r</mi><mi>t</mi></mrow></msub></mrow></math></span> score of 72.87% for summarization, and 58.22% for question answering. For transparency and reproducibility, all methodologies and code from this study are available on our GitHub repository: <span><span>https://github.com/zabir-nabil/bangla-multilingual-llm-eval</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"10 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bibliometric analysis of natural language processing using CiteSpace and VOSviewer","authors":"Xiuming Chen ,&nbsp;Wenjie Tian ,&nbsp;Haoyun Fang","doi":"10.1016/j.nlp.2024.100123","DOIUrl":"10.1016/j.nlp.2024.100123","url":null,"abstract":"<div><div>Natural Language Processing (NLP) holds a pivotal position in the domains of computer science and artificial intelligence (AI). Its focus is on exploring and developing theories and methodologies that facilitate seamless and effective communication between humans and computers through the use of natural language. First of all, In this paper, we employ the bibliometric analysis tools, namely CiteSpace and VOSviewer (Visualization of Similarities viewer) are used as the bibliometric analysis software in this paper to summarize the domain of NLP research and gain insights into its core research priorities. What is more, the Web of Science(WoS) Core Collection database serves as the primary source for data acquisition in this study. The data includes 4803 articles on NLP published from 2011 to May 15, 2024. The trends and types of articles reveal the developmental trajectory and current hotspots in NLP. Finally, the analysis covers eight aspects: volume of published articles, classification, countries, institutional collaboration, author collaboration network, cited author network, co-cited journals, and co-cited references. The applications of NLP are vast, spanning areas such as AI, electronic health records, risk, task analysis, data mining, computational modeling. The findings suggest that the emphasis of future research ought to focus on areas like AI, risk, task analysis, and computational modeling. This paper provides learners and practitioners with a comprehensive insight into the current status and emerging trends in NLP.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"10 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic-based temporal attention network for Arabic Video Captioning","authors":"Adel Jalal Yousif ,&nbsp;Mohammed H. Al-Jammas","doi":"10.1016/j.nlp.2024.100122","DOIUrl":"10.1016/j.nlp.2024.100122","url":null,"abstract":"<div><div>In recent years, there has been a surge in active research aiming to bridge the gap between computer vision and natural language. In a linguistically diverse region like the Arab world, it is essential to establish a mechanism that facilitates the understanding of visual aspects in native languages. Presents an Arabic video captioning method using an encoder–decoder paradigm based on CNN and LSTM. We employ a temporal attention mechanism along with semantic features to align keyframes with relevant semantic tags. Due to the lack of an Arabic captioning dataset, we use Google’s machine translation system to generate Arabic captions for the MSVD and MSR-VTT datasets, which can be used to train end-to-end Arabic video captioning models. The semantic features are extracted from a neural semantic representation network, which has been specifically trained on Arabic tags for better understanding. Semitic languages like Arabic are heavily attributed to complex morphology, which poses challenges for video captioning. We alleviate these difficulties by employing the AraBERT model as a preprocessing tool. Comprehensive experimental results demonstrate the superior performance of the proposed method compared to state-of-the-art models on two widely-used benchmarks: achieving a CIDEr score of 72.1% on MSVD and 38.0% on MSR-VTT.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"10 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data","authors":"Saroj Gopali ,&nbsp;Sima Siami-Namini ,&nbsp;Faranak Abri ,&nbsp;Akbar Siami Namin","doi":"10.1016/j.nlp.2024.100120","DOIUrl":"10.1016/j.nlp.2024.100120","url":null,"abstract":"<div><div>Generative AI, and in particular Large Language Models (LLMs), have gained substantial momentum due to their wide applications in various disciplines. While the use of these game changing technologies in generating textual information has already been demonstrated in several application domains, their abilities in generating complex models and executable codes need to be explored. As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generate the required models. This paper investigates and compares the performance of the mainstream LLMs, such as ChatGPT, PaLM, LLama, and Falcon, in generating deep learning models for analyzing time series data, an important and popular data type with its prevalent applications in many application domains including financial and stock market. This research conducts a set of controlled experiments where the prompts for generating deep learning-based models are controlled with respect to sensitivity levels of four criteria including (1) Clarify and Specificity, (2) Objective and Intent, (3) Contextual Information, and (4) Format and Style. While the results are relatively mix, we observe some distinct patterns. We notice that using LLMs, we are able to generate deep learning-based models with executable codes for each dataset separately whose performance are comparable with the manually crafted and optimized LSTM models for predicting the whole time series dataset. We also noticed that ChatGPT outperforms the other LLMs in generating more accurate models. Furthermore, we observed that the goodness of the generated models vary with respect to the “temperature” parameter used in configuring LLMS. The results can be beneficial for data analysts and practitioners who would like to leverage generative AIs to produce good prediction models with acceptable goodness.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"9 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lexical, sentiment and correlation analysis of sacred writings. A tale of cultural influxes and different ways to interpret reality","authors":"Alonso Felipe-Ruiz","doi":"10.1016/j.nlp.2024.100121","DOIUrl":"10.1016/j.nlp.2024.100121","url":null,"abstract":"<div><div>Natural Language Processing (NLP) has transformative potential for decoding sacred writings, bridging linguistic and temporal relationships between cultures. These texts, laden with cultural and religious significance. The study analyzes texts from 14 belief systems using lexical, sentiment and correlation assessment. The analysis revealed that sacred texts are complex due to archaic language, but tend to show similar themes, historical contexts, and emotional tones. The study highlights common terms found throughout the texts, but also revealing specific terms that are influenced by the cultural context of the belief system. It also explores the various depictions of fauna and flora, uncovering the impact of spatio-temporal contexts on the composition of sacred writings. Sentiment analysis reveals polarity variations between cultures and suggest conflicts of style during translation of the texts. Comparative analysis uncovers text clusters with high similarity and cultural influences between religions that coexisted. This work showcases NLP’s potential to enhance comprehension of sacred texts and promote cross-cultural understanding.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"9 ","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel application of deep learning to evaluate conversations from a mental health text support service","authors":"Daniel Cahn ,&nbsp;Sarah Yeoh ,&nbsp;Lakshya Soni ,&nbsp;Ariele Noble ,&nbsp;Mark A. Ungless ,&nbsp;Emma Lawrance ,&nbsp;Ovidiu Şerban","doi":"10.1016/j.nlp.2024.100119","DOIUrl":"10.1016/j.nlp.2024.100119","url":null,"abstract":"<div><div>The Shout text support service supports individuals experiencing mental health distress through anonymous text conversations. As one of the first research projects on the Shout dataset and one of the first significant attempts to apply advanced deep learning to a text messaging service, this project is a proof-of-concept demonstrating the potential of using deep learning to text messages. Several areas of interest to Shout are identifying texter characteristics, emphasising high suicide-risk participants, and understanding what can make conversations helpful to texters. Therefore, from a mental health perspective, we look at (1) characterising texter demographics strictly based on the vocabulary used throughout the conversation, (2) predicting an individual’s risk of suicide or self-harm, and (3) assessing conversation success by developing robust outcome metrics. To fulfil these aims, a series of Machine Learning models were trained using data from post-conversation surveys to predict the different levels of suicide risk, whether a conversation was helpful, and texter characteristics, such as demographic information. The results show that language models based on Deep Learning significantly improve understanding of this highly subjective dataset. We compare traditional methods and basic meta-features with the latest developments in Transformer-based architectures and showcase the advantages of mental health research.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"9 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conceptual commonsense-aware attentive modeling with pre-trained masked language models for humor recognition","authors":"Yuta Sasaki ,&nbsp;Jianwei Zhang ,&nbsp;Yuhki Shiraishi","doi":"10.1016/j.nlp.2024.100117","DOIUrl":"10.1016/j.nlp.2024.100117","url":null,"abstract":"<div><div>Humor is an important component of daily communication and usually causes laughter that promotes mental and physical health. Understanding humor is sometimes difficult for humans and may be more difficult for AIs since it usually requires deep commonsense. In this paper, we focus on automatic humor recognition by extrapolating conceptual commonsense-aware modules to Pre-trained Masked Language Models (PMLMs) to provide external knowledge. Specifically, keywords are extracted from an input text and conceptual commonsense embeddings associated with the keywords are obtained by using a COMET decoder. By using multi-head attention the representations of the input text and the commonsense are integrated. In this way we attempt to enable the proposed model to access commonsense knowledge and thus recognize humor that is not detectable only by PMLM. Through the experiments on two datasets we explore different sizes of PMLMs and different amounts of commonsense and find some sweet spots of PMLMs’ scales for integrating commonsense to perform humor recognition well. Our proposed models improve the F1 score by up to 1.7% and 4.1% on the haHackathon and humicroedit datasets respectively. The detailed analyses show our models also improve the sensitivity to humor while retaining the predictive tendency of the corresponding PMLMs.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"9 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}