Sentiment, Semantics, and Toxicity Detection

Source papers 63

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation
  This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomen…
• AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts
  Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse. While recent work focuses on …
• Artificial intelligence is ineffective and potentially harmful for fact checking
  Fact checking can be an effective strategy against misinformation 1,2,3, but its implementation at scale is impeded by the overwhelming volume of information online 4. Recent artificial intelligence (…
• Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
  “Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous work…
• Atesa-bært: A Heterogeneous Ensemble Learning Model For Aspect-based Sentiment Analysis
  The increasing volume of online reviews has made possible the development of sentiment analysis models for determining the opinion of customers regarding different products and services. Until now, se…
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
  The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fillin-the-blanks problems (e.g., …
• Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
  Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form …
• Beyond the Surface: Probing the Ideological Depth of Large Language Models
  Large Language Models (LLMs) have demonstrated pronounced ideological leanings, yet the stability and depth of these positions remain poorly understood. Surface-level responses can often be manipulate…
• CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning
  Large Language Models (LLMs) have recently achieved impressive results in complex reasoning tasks through Chain of Thought (CoT) prompting. However, most existing CoT methods rely on using the same pr…
• Can Authorship Representation Learning Capture Stylistic Features?
  “Knowing something about an author’s writing style is helpful in many applications, such as predicting who the author is, determining which passages of a document the author composed, rephrasing text …
• Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation
  Ambiguous words are often found in modern digital communications. Lexical ambiguity challenges traditional Word Sense Disambiguation (WSD) methods, due to limited data. Consequently, the efficiency of…
• Can Large Language Models Transform Computational Social Science?
  Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social ph…
• Classifying YouTube Comments Based on Sentiment and Type of Sentence
  As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expe…
• Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction
  Keyphrase extraction is the task of identifying a set of keyphrases present in a document that captures its most salient topics. Scientific domain-specific pre-training has led to achieving state-of-t…
• Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews
  “Deciding on a product to purchase can be a time-consuming process. Every user has specific quality preferences, budget restrictions, or enjoys different item features. To distill important informatio…
• Conversation Derailment Forecasting with Graph Convolutional Networks
  Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse. Forecasting conversa…
• Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
  What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue – whether betwe…
• Creativity Has Left the Chat: The Price of Debiasing Language Models
  Our findings reveal that aligned models exhibit lower entropy in token predictions, form distinct clusters in the embedding space, and gravitate towards “attractor states”, indicating limited output d…
• DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations
  Those models take a contrastive learning approach, where they build binary classifiers to differentiate positive, or coherent examples from negative, or incoherent dialogues. Those classifiers are usu…
• DeepCT-enhanced Lexical Argument Retrieval
  The recent Touché lab’s argument retrieval task focuses on controversial topics like ‘Should bottled water be banned?’ and asks to retrieve relevant pro/con arguments. Interestingly, the most effectiv…
• Detecting Deception Using Natural Language Processing and Machine Learning in Datasets on COVID-19 and Climate Change
  In today’s world of fast-growing technology and an inexhaustible amount of data, there is a great need to control and verify data validity due to the possibility of fraud. Therefore, the need for a re…
• Detoxify Language Model Step-by-Step
  Detoxification for LLMs is challenging since it requires models to avoid generating harmful content while maintaining the generation capability. To ensure the safety of generations, previous detoxific…
• Evaluating Emotional Nuances In Dialogue Summarization
  Automatic dialogue summarization is a well-established task that aims to identify the most important content from human conversations to create a short textual summary. Despite recent progress in the …
• Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
  In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use…
• Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models
  The advent of social media has given rise to numerous ethical challenges, with hate speech among the most significant concerns. Researchers are attempting to tackle this problem by leveraging hate-spe…
• Exploring the Role of Prior Beliefs for Argument Persuasion
  Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the langu…
• Fake News Detectors are Biased against Texts Generated by Large Language Models
  The spread of fake news has emerged as a critical challenge, undermining trust and posing threats to society. In the era of Large Language Models (LLMs), the capability to generate believable fake con…
• Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
  method leverages the inherent vulnerabilities of LLMs in handling world knowledge, which can be exploited by attackers to unconsciously spread fabricated information. Through extensive experiments, we…
• Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
  Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims…
• HonestBait: Forward References for Attractive but Faithful Headline Generation
  Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do reflect user interest…
• How new data permeates LLM knowledge and how to dilute it
  Large language models learn and continually learn through the accumulation of gradient-based updates, but how individual pieces of new information affect existing knowledge, leading to both beneficial…
• Improving Document-Level Sentiment Analysis with User and Product Context
  Past work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. We investigate incorporating add…
• Inducing Positive Perspectives with Text Reframing
  Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a reversal in meaning. We i…
• Irony in Emojis: A Comparative Study of Human and LLM Interpretation
  irony poses a significant challenge for Large Language Models (LLMs) due to its inherent incongruity between appearance and intent. This study examines the ability of GPT-4o to interpret irony in emoj…
• LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
  Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropri…
• LLMs as Architects and Critics for Multi-Source Opinion Summarization
  benchmark dataset for evaluating multi-source opinion summaries across 7 key dimensions: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, specificity. Our results d…
• Large Concept Models: Language Modeling in a Sentence Representation Space
  This is in sharp contrast to humans who operate at multiple levels of abstraction, well beyond single words, to analyze information and to generate creative content. In this paper, we present an attem…
• Large Language Models Can Infer Psychological Dispositions of Social Media Users
  Large Language Models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychologica…
• Linguistic markers of inherently false AI communication and intentionally false human communication: Evidence from hotel reviews
  To the human eye, AI-generated outputs of large language models have increasingly become indistinguishable from human-generated outputs. Therefore, to determine the linguistic properties that separate…
• Man vs machine – Detecting deception in online reviews
  This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based …
• Measuring the Value of Social Dynamics in Online Product Ratings Forums
  “The common underlying assumption of studies which investigate the impact of consumer reviews on product sales is that posted product ratings reflect the customers’ experience with the product, indepe…
• Modeling Appropriate Language in Argumentation
  Online discussion moderators must make adhoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offens…
• News Sentiment Embeddings for Stock Price Forecasting
  A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encod…
• On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
  The ability of Large Language Models (LLMs) to encode syntactic and semantic structures of language is well examined in NLP. Additionally, analogy identification, in the form of word analogies are ext…
• Operating Multi-Client Influence Networks Across Platforms
  This report outlines several case studies on how actors have misused our models, as well as the steps we have taken to detect and counter such misuse. By sharing these insights, we hope to protect the…
• Overview of DialAM-2024: Argument Mining in Natural Language Dialogues
  Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been …
• PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes
  Large language model (LLM) personalization aims to align model outputs with individuals’ unique preferences and opinions. While recent efforts have implemented various personalization methods, a unifi…
• Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support
  Multiple studies on content moderation have identified a problem of scale: even if antisocial behavior is a small fraction of all content that gets posted, the sheer size of modern online platforms, t…
• Proxona: Leveraging LLM-Driven Personas to Enhance Creators' Understanding of Their Audience
  we present Proxona, a system for defining and extracting representative audience personas from the comments. Creators converse with personas to gain insights into their preferences and engagement, sol…
• Quantifying Controversy on Social Media
  Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the filtering and aggregation of social medi…
• Recommendation systems and convergence of online reviews: The type of product network matters!
  “The Internet has given word-of-mouth (WOM) a new significance by allowing individuals to express their opinions and thoughts to a global audience, and so, it is an essential aspect of e-commerce [3].…
• Rethinking STS and NLI in Large Language Models
  Recent years, have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks. However, when applied to semanti…
• SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding
  “Social media (SM) plays an increasingly important role in our lives. As of 2021, seven out of ten US adults use at least one social media platform like Facebook, Twitter, Instagram, or Pinterest [3].…
• SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM
  Abstract—Topic discovery in scientific literature provides valuable insights for researchers to identify emerging trends and explore new avenues for investigation, facilitating easier scientific infor…
• Semantic Specialization for Knowledge-based Word Sense Disambiguation
  A promising approach for knowledge-based Word Sense Disambiguation (WSD) is to select the sense whose contextualized embeddings computed for its definition sentence are closest to those computed for a…
• Semantic Structure in Large Language Model Embeddings
  Psychological research consistently finds that human ratings of words across diverse semantic scales can be reduced to a low-dimensional form with relatively little information loss. We find that the …
• The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
  Abstract—Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Speci…
• The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
  Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-tur…
• Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
  Automated counter-narratives (CN) offer a promising strategy for mitigating online hate speech, yet concerns about their affective tone, accessibility and ethical risks remain. We propose a framework …
• Transformer-based cynical expression detection in a corpus of Spanish YouTube reviews
  Consumers of services and products exhibit a wide range of behaviors on social networks when they are dissatisfied. In this paper, we consider three types of cynical expressions – negative feelings, s…
• Truth or lie: Exploring the language of deception
  Lying appears in everyday oral and written communication. As a consequence, detecting it on the basis of linguistic analysis is particularly important. Our study aimed to verify whether the difference…
• Using Computational Models to Test Syntactic Learnability
  We study the learnability of English filler—gap dependencies and the “island” con- straints on them by assessing the generalizations made by autoregressive (incremental) language models that use deep …
• Word Meanings in Transformer Language Models
  We investigate how word meanings are represented in the transformer language models. Specifically, we focus on whether transformer models employ something analogous to a lexical store - where each wor…