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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Sentiment Analysis of News Headlines for Stock Market Prediction using VADER 3

Sentiment Analysis of News Headlines for Stock Market Predic...

引用

3rd International conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Soni, Jitendra Mathur, Kirti Devi Ahilya Vishwavidyalaya Institute of Engineering & Technology Indore India Devi Ahilya Vishwavidyalaya International Institute of Professional Studies Indore India

ISBN: (纸本)9798350343632

In today's world, everyone, from novices to experts, seeks to understand and profit from the complex stock market. However, predicting stock price movements remains a daunting task due to its frequent fluctuations. Companies worldwide rely on these predictions to boost profits. This study focuses on the immediate impact of news headlines on a company's stock performance. Using natural language processing (NLP), this study compares two methods: one using traditional machine learning algorithms and the other leveraging VADER sentiment analysis. These methods assess whether news headlines are positive, negative, or neutral and their influence on stock prices. Our research highlights the superior accuracy of the VADER-based approach in predicting stock market trends, offering a more reliable tool for investors and businesses in navigating the ever-changing financial landscape. © 2023 IEEE.

关键词： Lexicon-based methods News headlines Sentimental Analysis Stock market prediction Valence Aware Dictionary and Sentiment Reasoner

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Burnout and Depression Detection Using Affective Word List Ratings

Burnout and Depression Detection Using Affective Word List R...

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International conference on Healthcare of the Future - Digital Health - From Vision to Best Practice

作者： Haug, Sophie Kurpicz-Briki, Mascha Bern Univ Appl Sci Appl Machine Intelligence Biel Switzerland

ISBN: (纸本)9781643682815;9781643682808

Burnout syndrome and depression are prevalent mental health problems in many societies today. Most existing methods used in clinical intervention and research are based on inventories. natural language processing (NLP) enables new possibilities to automatically evaluate text in the context of clinical Psychology. In this paper, we show how affective word list ratings can be used to differentiate between texts indicating depression or burnout, and a control group. In particular, we show that depression and burnout show statistically significantly higher arousal than the control group.

关键词： psychology natural language processing affective word lists augmented intelligence

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Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in language

Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambigu...

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2021 conference on empirical methods in natural language processing, EMNLP 2021

作者： Efrat, Avia Shaham, Uri Kilman, Dan Levy, Omer Tel Aviv University Israel

ISBN: (纸本)9781955917094

Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models;fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%). © 2021 Association for Computational Linguistics

关键词： Large dataset

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Cyberbullying Detection in Hinglish language

Cyberbullying Detection in Hinglish Language

引用

2024 International conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Rajiv Rai, Vipin Joshi, Shivani Raj, Daksh Amrita Galgotias University Department of Computer Science & Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350378092

Cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing. © 2024 IEEE.

关键词： Phishing

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An empirical Comparison of Machine Learning methods for Text-Based Sentiment Analysis of Online Consumer Reviews

An Empirical Comparison of Machine Learning Methods for Text...

引用

作者： Alantari, Huwail J. University of California Irvine

学位级别：Ph.D., Doctor of Philosophy

The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer’s overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.

关键词： Automated text analysis Machine learning natural language processing Online reviews Sentiment analysis User generated content Prediction and diagnostics

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Interactive RF Game Design for Deciphering Real-World Human Motion: Activities, Gestures, and Sign language

Interactive RF Game Design for Deciphering Real-World Human ...

引用

IEEE Radar conference (RadarConf)

作者： Gurbuz, Sevgi Z. Crawford, Chris Griffin, Darrin J. Kurtoglu, Emre Adeoluwa, Oladipupo Haeker, Josh Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Univ Alabama Dept Commun Studies Tuscaloosa AL USA

ISBN: (纸本)9781665436694

Current methods for acquiring datasets for human motion classification are limited to controlled settings where participants are directed by a human experiment organizer. Datasets acquired in controlled settings often cannot capture natural human behaviors and are inadequate for obtaining large amounts of real-world data in a sustainable fashion. This paper proposes a new paradigm for automated acquisition of natural human movements based on Interactive RF Gaming. The training of AI/ML models is considered over an evolution of time: pre-deployment via physics-aware batch training and post-deployment via continual learning from interactions. Algorithms needed to address real-world considerations such as the parsing of continuous streams of data, computational constraints, real-time processing, and game design considerations based on cyberphysical human system requirements are also discussed.

关键词： micro-Doppler RF sensing radar human-computer interaction human sensing human activity recognition gesture recognition sign language

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A Systematic Review of Alzheimer's disease detection based on speech and natural language processing 32

A Systematic Review of Alzheimer's disease detection based o...

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32nd International conference on Radioelectronics (RADIOELECTRONICS)

作者： Sevcik, Adam Rusko, Milan Slovak Acad Sci Inst Informat Bratislava Slovakia Slovak Univ Technol Bratislava Fac Informat & Informat Technol Bratislava Slovakia

ISBN: (纸本)9781728186863

Objectives: The main goal of this review is to introduce the work with various conversational datasets containing data from patients suffering from Alzheimer's disease. The basic questions we deal with in the systematic review are: (1) Which datasets are most often used in studies? (2) How does the number of participants in individual datasets vary? (3) What is the representation of individual groups in datasets? methods: We used databases Scopus, Web of Science and Google Scholar to create the report. Inclusion criteria have been created from key words - voice, speech, Alzheimer detection and natural language processing. We have focused on articles from the beginning of 2019 until now. Articles that did not contain full-text in English were excluded. Results: The review contains a total of 37 studies in which their datasets can be examined. The most commonly used datasets in the articles are ADReSS, PITT and CCC. Many datasets were created for a specific study only, and some have not yet been made public. The range of participants in individual datasets ranges from 30 to 865. The most frequently used ADReSS dataset in the studies contained records of 156 participants. Conclusion: In several cases, the size of the dataset turns out to play an important role, but the overall quality of the dataset was a more significant factor. As a result of a deeper understanding of datasets, we concluded that many factors, such as the age of the participants, gender, or the number of education years, played a significant role as an indicator for Alzheimer's disease prediction.

关键词： Alzheimer detection speech natural language processing systematic review PRISMA

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A Numeral and Affective Knowledge Enhanced Network for Aspect-based Financial Sentiment Analysis 35

A Numeral and Affective Knowledge Enhanced Network for Aspec...

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35th IEEE International conference on Tools with Artificial Intelligence (ICTAI)

作者： Qin, Chuan Yu, Changrui Meng, Yuan Chang, Jun Shanghai Univ Finance & Econ Sch Informat Management & Engn Shanghai Peoples R China Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai Peoples R China

ISBN: (纸本)9798350342734

Aspect-based financial sentiment analysis (ABFSA) is a fine-grained task that can enrich and sharpen financial analysis by identifying sentiments towards specific entities (e.g., company, stock). As the application of ABSA in professional language, ABFSA is a challenging task requiring extensive domain knowledge while remaining understudied. Numeral understanding is crucial for financial text analysis, but existing NLP models for ABFSA lack such ability by mainly treating numerals as plain text. In addition, most studies on knowledge incorporation disregard necessary domain-specific connotations or suffer from the low coverage issue. In this paper, we propose a novel numeral-oriented network with a multi-source affective knowledge refinement strategy (NumAKEN) for ABFSA. NumAKEN utilizes a numeral encoding method based on DigitCNN to capture critical numeric concepts such as magnitude and category. A multi-source affective knowledge fusion strategy is designed for hybrid lexicon construction and incorporation, which can guide the model to capture significant sentiment clues as well as alleviate conflicting and coverage issues. Extensive experiments on two datasets illustrate that our NumAKEN model outperforms all state-of-the-art methods and verify the effectiveness of our model.

关键词： financial sentiment analysis numeral understanding domain knowledge deep learning natural language processing

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IMPROVING BRAIN DECODING methods AND EVALUATION 47

IMPROVING BRAIN DECODING METHODS AND EVALUATION

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Pascual, Damian Egressy, Beni Affolter, Nicolas Cai, Yiming Richter, Oliver Wattenhofer, Roger Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665405409

Brain decoding, understood as the process of mapping brain activities to the stimuli that generated them, has been an active research area in the last years. In the case of language stimuli, recent studies have shown that it is possible to decode fMRI scans into an embedding of the word a subject is reading. However, such word embeddings are designed for natural language processing tasks rather than for brain decoding. Therefore, they limit the model's ability to recover the precise stimulus. In this work, we propose to directly classify an fMRI scan, mapping it to the corresponding word within a fixed vocabulary. Unlike existing work, we evaluate on scans from previously unseen subjects. We argue that this is a more realistic setup and we present a model that can decode fMRI data from unseen subjects with 2:62% Top-1 and 9:76% Top-5 accuracy in this challenging task. Moreover our model can be fine-tuned on data from the test subject to achieve 4:22% Top-1 and 12:87% Top-5 accuracy, significantly outperforming all the considered competitive baselines.

关键词： fMRI classification word embeddings

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On the Machine Learning of Ethical Judgments from natural language

On the Machine Learning of Ethical Judgments from Natural La...

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conference of the North-American-Chapter-of-the-Association-for-Computational-Linguistics (NAAACL) - Human language Technologies

作者： Talat, Zeerak Blix, Hagen Valvoda, Josef Ganesh, Maya Indira Cotterell, Ryan Williams, Adina Digital Democracies Inst Burnaby BC Canada NYU New York NY 10003 USA Univ Cambridge Cambridge England Swiss Fed Inst Technol Zurich Switzerland Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781955917711

Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to address ethical issues of harmful outcomes in machine learning systems that are made to interface with humans. One recent approach in this vein is the construction of NLP morality models that can take in arbitrary text and output a moral judgment about the situation described. In this work, we offer a critique of such NLP methods for automating ethical decision-making. Through an audit of recent work on computational approaches for predicting morality, we examine the broader issues that arise from such efforts. We conclude with a discussion of how machine ethics could usefully proceed in NLP, by focusing on current and near-future uses of technology, in a way that centers around transparency, democratic values, and allows for straightforward accountability.

关键词： natural language processing systems

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