The relationship between language and thought remains an unresolved philosophical issue. Existing viewpoints can be broadly categorized into two schools: one asserting their independence, and another arguing that language constrains thought. In the context of large language models, this debate raises a crucial question: Does a language model's grasp of semantic meaning depend on thought processes? To explore this issue, we investigate whether reasoning techniques can facilitate semantic understanding. Specifically, we conceptualize thought as reasoning, employ chain-of-thought prompting as a reasoning technique, and examine its impact on sentiment analysis tasks. The experiments show that chain-of-thought has a minimal impact on sentiment analysis tasks. Both the standard and chain-of-thought prompts focus on aspect terms rather than sentiment in the generated content. Furthermore, counterfactual experiments reveal that the model's handling of sentiment tasks primarily depends on information from demonstrations. The experimental results support the first viewpoint.
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
The College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Harbin Institute of Technology（哈尔滨工业大学）
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
John F Kennedy High school Fremont, USA（约翰·F·肯尼迪高中弗里蒙特分校）；Queensland Academy for Science Mathematics and Technology（昆士兰科学数学与技术学院）；Research Spark Hub Inc（Research Spark Hub公司）
MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Academy of Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shanghai Jiao Tong University（上海交通大学）；School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）
Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies
Hong Kong Baptist University（香港浸会大学）；BNU-UIC Institute of AI and Future Networks（北师大-联合国际学院人工智能与未来网络研究院）；BNU-HKBU United International College（北师大-港浸大联合国际学院）；Beijing Normal University（北京师范大学）
The Ohio State University（俄亥俄州立大学）；Google Research（谷歌研究院）
Market sentiment analysis on social media content requires knowledge of both financial markets and social media jargon, which makes it a challenging task for human raters. The resulting lack of high-quality labeled data stands in the way of conventional supervised learning methods. Instead, we approach this problem using semi-supervised learning with a large language model (LLM). Our pipeline generates weak financial sentiment labels for Reddit posts with an LLM and then uses that data to train a small model that can be served in production. We find that prompting the LLM to produce Chain-of-T…
C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis
School of Computer Science, South China Normal University（华南师范大学计算机科学学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Fujita Health University（藤田医科大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Causal Prompting for Implicit Sentiment Analysis with Large Language Models
School of Computing Technologies, RMIT University（RMIT大学计算机技术学院）；Institute of Innovation, Science and Sustainability, Federation University Australia（联邦大学澳大利亚创新、科学与可持续性研究所）；CSIRO’s Data61（CSIRO数据61）
Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Electronic Science and Engineering (School of Microelectronics)（广东量子工程与材料重点实验室，电子科学与工程学院（微电子学院））；South China Normal University（华南师范大学）；Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东智能信息处理重点实验室）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑技术大学）
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Monash University（莫纳什大学）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什研究学院）；IIT-BHU (Varanasi)（印度理工学院贝纳拉斯印度教大学分校（瓦拉纳西））
Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports a simple topic model for sarcasm detection, a first, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluatio…
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The Hong Kong Polytechnic University（香港理工大学）；College of Software Engineering, Zhengzhou University of Light Industry（郑州轻工业大学软件学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Information Technology, Halmstad University（哈尔姆斯塔德大学信息技术学院）
Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification. However, because the performances of senten…
Universitas Islam Riau（廖内伊斯兰大学）；Universitas Lancang Kuning（兰昌昆宁大学）；University of Riau（廖内大学）
Twitter is one of the top influenced social media which has a million number of active users. It is commonly used for microblogging that allows users to share messages, ideas, thoughts and many more. Thus, millions interaction such as short messages or tweets are flowing around among the twitter users discussing various topics that has been happening world-wide. This research aims to analyse a sentiment of the users towards a particular trending topic that has been actively and massively discussed at that time. We chose a hashtag \textit{\#kpujangancurang} that was the trending topic during th…
Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and tools. One of these challenges is code-mixing, which means using different languages to convey thoughts in social media texts. Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-…
In present century, data volume is increasing enormously. The data could be in form for image, text, voice, and video. One factor in this huge growth of data is usage of social media where everyone is posting data on daily basis during chatting, exchanging information, and uploading their personal and official credential. Research of sentiments seeks to uncover abstract knowledge in Published texts in which users communicate their emotions and thoughts about shared content, including blogs, news and social networks. Roman Urdu is the one of most dominant language on social networks in Pakistan…
Humor is a natural and fundamental component of human interactions. When correctly applied, humor allows us to express thoughts and feelings conveniently and effectively, increasing interpersonal affection, likeability, and trust. However, understanding the use of humor is a computationally challenging task from the perspective of humor-aware language processing models. As language models become ubiquitous through virtual-assistants and IOT devices, the need to develop humor-aware models rises exponentially. To further improve the state-of-the-art capacity to perform this particular sentiment-…
We apply the Natural Semantic Metalanguage (NSM) approach (Goddard and Wierzbicka 2014) to the lexical-semantic analysis of English evaluational adjectives and compare the results with the picture developed in the Appraisal Framework (Martin and White 2005). The analysis is corpus-assisted, with examples mainly drawn from film and book reviews, and supported by collocational and statistical information from WordBanks Online. We propose NSM explications for 24 evaluational adjectives, arguing that they fall into five groups, each of which corresponds to a distinct semantic template. The groups …
Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，哈马德·本·哈利法大学）
Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and resear…
Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who …
University of Toronto（多伦多大学）；Canadian Institute for Advanced Research（加拿大高等研究院）；Massachusetts Institute of Technology（麻省理工学院）
We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models o…
NepEMO: A Multi-Label Emotion and Sentiment Analysis on Nepali Reddit with Linguistic Insights and Temporal Trends
Department of Computer Science and Application（计算机科学与应用系）；Mechi Multiple Campus, Tribhuvan University（特里布文大学梅奇多校园）；School of Computer Science（计算机科学学院）；Queensland University of Technology（昆士兰理工大学）；Department of Electronics and Computer Engineering（电子与计算机工程系）；Advanced College of Engineering and Management, Tribhuvan University（特里布文大学先进工程与管理学院）；School of Engineering and Technology（工程与技术学院）
Large Vision-Language Models as Emotion Recognizers in Context Awareness
Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Cognition and Intelligent Technology Laboratory（认知与智能技术实验室）；Engineering Research Center of AI and Robotics（人工智能与机器人工程研究中心）；AI and Unmanned Systems Engineering Research Center of Jilin Province（吉林省人工智能与无人系统工程研究中心）
Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks. We reconsider rationale-augmented prompting for few-shot in-context learning, where (input -> output) prompts are expanded to (input, rationale -> output) prompts. For rationale-augmented prompting we demonstrate how existing approaches, which rely on manual prompt engineering, are subject to sub-optimal rationales that may harm performance. To mitigate this brittleness, we propose a unified framework of rationale-augmented ensembles, where we identify…
