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Sentiment analysis application and natural language processing for mobile network operators' support on social media 2

Sentiment analysis application and natural language processi...

引用

2nd international conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 2019

作者： Ogudo, Kingsley A. Nestor, Dahj Muwawa Jean Department of Electrical and Electronics Engineering Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa University of South Africa Pretoria South Africa

ISBN: (纸本)9781538692363

Social Media have become a mixed platform of emotional expressions on services and products reviews. While network operators focus on quality of service and customer experience, mostly built upon complaints and performance indicators, subscribers and/or followers are mostly expressing their emotions on twitter and other social media. On one side, understanding followers' sentiment and perception on offered and applied services can help the South African mobile network operators to anticipate network and customer problems, embracing proactive measures rather than reactive ones on improving service and network quality. On the other side, the rise of text mining, sentiment analysis and the global natural language processing expands the needs of Data Analysis across textual statistics. In this paper, we leverage on natural language processing (NLP), using sentiment analysis and text mining to analyze mobile network operators, in this case CellC followers' using the R platform. We use the polarity model of sentiment analysis to determine the level of potential detraction and promotion across South African Mobile Network Operator (MNO), based on public tweets. The orientation of the study in this paper creates a bi-directional link between customers or followers and the MNO, in the goal of extracting relevant signification from sets of social media's unstructured information. © 2019 IEEE.

关键词： Sentiment analysis

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Event recognition based on time series characteristics

Event recognition based on time series characteristics

引用

2011 8th international conference on Fuzzy Systems and knowledge Discovery, FSKD 2011, Jointly with the 2011 7th international conference on natural Computation, ICNC'11

作者： Li, Fenghuan Zheng, Dequan Zhao, Tiejun MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China

ISBN: (纸本)9781612841816

Event recognition and temporal information analysis are important subtasks in information extraction (IE). In this paper, event recognition based on time series characteristics is proposed. In the pipeline of event recognition, trigger word table is extracted from training corpus and extended based on the field and thesaurus, which is regarded as a priori knowledge. Then event recognition is carried out using trigger words and support vector machine (SVM). Temporal expressions are normalized primarily when recognizing event time. Especially, keywords on time and their priorities are taken into account. Finally, events are sorted by time series characteristics. The results show that methods proposed in this paper are valid and effective. © 2011 IEEE.

关键词： Support vector machines

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Breaking Barriers: A Hands-On Tutorial on AI-Enabled Accessibility to Social Media Content 24

Breaking Barriers: A Hands-On Tutorial on AI-Enabled Accessi...

引用

30th ACM SIGKDD conference on knowledge Discovery and Data Mining

作者： Villena, Julio Catala, Rosa Garcia, Janine Polo, Concepcion Labrador, Yessika Del Valle, Francisco Ayyagari, Bhargav Reddit Inc Madrid Spain Reddit Inc San Francisco CA USA Reddit Inc Toronto ON Canada

ISBN: (纸本)9798400704901

Reddit's mission is to bring community, belonging, and empowerment to everyone in the world. This hands-on tutorial explores the immense potential of Artificial Intelligence (AI) to improve accessibility to social media content for individuals with different disabilities, including hearing, visual, and cognitive impairments. We will design and implement a variety of AI-based approaches based on multimodal open-source Large language Models (LLMs) to bridge the gap between research and real-world applications.

关键词： AI LLM accessibility inclusive content multimodal content social media natural language processing speech to text

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Analyzing Textual Data for Mental Health Assessment: natural language processing for Depression and Anxiety 10

Analyzing Textual Data for Mental Health Assessment: Natural...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Dixit, Krishna Kant Pundir, Sumit Shrivastava, Anurag Kumar, C Praveen Srivastava, Arun Pratap Singh, Pankaj Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

In this study, depression and anxiety markers found in text data are carefully targeted in order to use natural language processing (NLP) techniques to the evaluation of mental health. The work gives a thorough assessment of NLP models that include sentiment analysis, emotion identification, as well as linguistic pattern detection along with shows significant improvements in F1 scores, recall, and accuracy. The varied dataset, gathered from social media, online discussion boards, and healthcare records, is rigorously preprocessed as well as qualitatively evaluated, enhancing the validity of the method. The findings might advance early identification and intervention by bridging the technical and mental health spheres. Cross-cultural datasets, multi-modal data integration, moral concerns, and cooperation with mental health specialists are important future prospects. © 2023 IEEE.

关键词： Anxiety Depression Emotional Recognition Mental Health Assessment natural language processing Sentiment Analysis

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Cyberattack Prediction Through Public Text Analysis and Mini-Theories

Cyberattack Prediction Through Public Text Analysis and Mini...

引用

IEEE international conference on Big Data (Big Data)

作者： Perera, Ian Hwang, Jena Bayas, Kevin Dorr, Bonnie Wilks, Yorick Florida Inst Human & Machine Cognit Ocala FL 34471 USA

ISBN: (纸本)9781538650356

This paper describes a new approach to detection and tracking of potential cyberattacks from analyzing large quantities of cyber-related webpage text, using ontological knowledge about such attacks combined with composable causal models represented in Probabilistic Soft Logic. The stages of a cyberattack kill chain are viewed as a sequence of both observed and unobserved events (e.g., reconnaissance, weaponize, exploit, install) and explicit mentions of, or related to, such events are examined as potential signals for a future attack. Using a suite of natural language processing techniques, sentences from input news texts are automatically classified according to the described cyberattack event, then enriched with named entity recognition for the rapid detection of key elements that might be associated with potential cyberattacks. We present our work as a framework for rapid and flexible predictive analysis of the ever-increasing amount of cyber-related text data, with initial experiments indicating that event detection using parsing and named entity recognition combined with statistical relational learning show promise in time-series prediction from news text.

关键词： Cybersecurity Statistical Relational Learning natural language processing Big Data

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Semi-automatic 3D virtual surgery environment generation from operative surgery manuals

Semi-automatic 3D virtual surgery environment generation fro...

引用

2012 international conference on Systems and Informatics, ICSAI 2012

作者： Li, Xiang Xiang, Hui Lin, Jiang School of Computer Science and Technology Shandong University Jinan China

ISBN: (纸本)9781467301992

Recently, virtual surgery technologies have much progress and many simulators have been developed for education, planning rehearsal and so on. However, development of VR-based surgical simulator takes much more labor and cost not only for implementing simulation modules but also for setting surgical environment. Considering that operative surgery manuals describe the knowledge of manipulations and the needed sources in surgical procedure and could be of great help to facilitate the work of building 3D virtual surgery environment effectively. In this study, we use the natural language processing technology to extract the information contained in the operative surgery manual, such as, the anatomical objects and surgical procedures. Then, we propose a framework that combines the natural language processing techniques and the surgical simulation technology together. In the framework, combined with medical ontological technologies, NLP modules are adapted to extract surgical information and XML file were made, which then be utilized by simulation modules using a simulation library in different system configurations to generate the final surgery simulator. © 2012 IEEE.

关键词： natural language processing systems

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Cyber Threat Intelligence Entity Extraction Based on Deep Learning and Field knowledge engineering 25

Cyber Threat Intelligence Entity Extraction Based on Deep Le...

引用

25th IEEE international conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Xuren Liu, Runshi Yang, Jie Chen, Rong Ling, Zhiting Yang, Peian Zhang, Kai Capital Normal University Information Engineering College Beijing China Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9781665405270

The typical domain characteristics of Cyber Threat Intelligence (CTI), such as fuzzy entity boundary, polysemy or a single word corresponding to multiple word expressions and so on, makes the entity recognition result be worse than we expected. In addition, there are many challenges in directly migrating entity recognition models from the general field to CTI field. Therefore, we propose a deep learning entity recognition model with supplementing the domain knowledge engineering, which takes the open-source Cyber Threat Intelligence entity recognition as the research object, covering natural language processing, deep learning and cyber threat intelligence fields. Firstly, we use BERT model to obtain the dynamic word vector, then encode the word sequence by using BiLSTM-CRF, and finally improve the recognition result by using knowledge engineering of the Cyber Threat Intelligence to help increase the accuracy of entity recognition. Besides, we verify the effectiveness of the proposed model by experiments. © 2022 IEEE.

关键词： Deep learning

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Constructing an anaphorically annotated corpus with non-experts: Assessing the quality of collaborative annotations 1

Constructing an anaphorically annotated corpus with non-expe...

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1st Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic Resources, People's Web 2009 at the Joint conference of the 47th Annual Meeting of the ACL and the 4th international Joint conference on natural language processing of the AFNLP, ACL-IJCNLP 2009

作者： Chamberlain, Jon Kruschwitz, Udo Poesio, Massimo University of Essex School of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)1932432558

This paper reports on the ongoing work of Phrase Detectives, an attempt to create a very large anaphorically annotated text corpus. Annotated corpora of the size needed for modern computational linguistics research cannot be created by small groups of hand-annotators however the ESP game and similar games with a purpose have demonstrated how it might be possible to do this through Web collaboration. We show that this approach could be used to create large, high-quality natural language resources. © 2009 ACL and AFNLP

关键词： natural language processing systems

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Fine-Tuned Transformer Models for Question Answering 14

Fine-Tuned Transformer Models for Question Answering

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Lakkimsetty, Sai Satvikh Latchireddy, Sai Varshini Lakkoju, Sai Manikanta Manukonda, Gopalakrishna Reddy Krishna, R. V. V. Murali Computer Science and Engineering Vishakapatnam India Information Technology Vishakapatnam India

ISBN: (纸本)9798350335095

Effective question-answering is crucial for applications involving natural language processing such as conversational artificial systems. The pre-trained BERT (Bidirectional Encoder Representations from Transformers) model achieved outstanding results on the Stanford Question Answering Dataset (SQuAD), a well-known benchmark for question answering tasks. The three different BERT models (BERT-base, BERT-large, DistilBERT) are pre-trained on general text corpus. These BERT models are trained to identify the right context in a paragraph to give answers. Further, fine-tuning the models with few-shot learning on domain related questions and answers improves the performance. Hence, our work proposes different strategies to improve the performance of BERT models on tasks requiring domain-specific question-answering tasks using a customized SQuAD dataset. Our work highlights the significance of optimizing the BERT models on domain-specific data for enhanced performance in particular tasks. The results of this study have implications for domain-specific knowledge in natural language processing. With a score of 0.9632, the BERT-Large model has the best accuracy out of all the BERT models tested. The performance of the BERT-Large model consistently beats that of the other models in several parameters, including precision, recall, and F1 score. © 2023 IEEE.

关键词： Domain knowledge

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QATest: A Uniform Fuzzing Framework for Question Answering Systems 7

QATest: A Uniform Fuzzing Framework for Question Answering S...

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37th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Liu, Zixi Feng, Yang Yin, Yining Sun, Jingyu Chen, Zhenyu Xu, Baowen Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9781450394758

The tremendous advancements in deep learning techniques have empowered question answering(QA) systems with the capability of dealing with various tasks. Many commercial QA systems, such as Siri, Google Home, and Alexa, have been deployed to assist people in different daily activities. However, modern QA systems are often designed to deal with different topics and task formats, which makes both the test collection and labeling tasks difficult and thus threats their quality. To alleviate this challenge, in this paper, we design and implement a fuzzing framework for QA systems, namely QATest, based on the metamorphic testing theory. It provides the first uniform solution to generate tests with oracle information automatically for various QA systems, such as machine reading comprehension, open-domain QA, and QA on knowledge bases. To further improve testing efficiency and generate more tests detecting erroneous behaviors, we design N-Gram coverage and perplexity priority based on the features of the question data to guide the generation process. To evaluate the performance of QATest, we experiment with it on four QA systems that are designed for different tasks. The experiment results show that the tests generated by QATest detect hundreds of erroneous behaviors of QA systems efficiently. Also, the results confirm that the testing criteria can improve test diversity and fuzzing efficiency.

关键词： fuzz testing automated testing question answering systems natural language processing

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