Tootfinder

This https://arxiv.org/abs/2403.19335 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes
This paper presents KazSAnDRA, a dataset developed for Kazakh sentiment analysis that is the first and largest publicly available dataset of its kind. KazSAnDRA comprises an extensive collection of 180,064 reviews obtained from various sources and includes numerical ratings ranging from 1 to 5, providing a quantitative representation of customer attitudes. The study also pursued the automation of Kazakh sentiment classification through the development and evaluation of four machine learning mod…

Revitalising Stagecraft: NLP-Driven Sentiment Analysis for Traditional Theater Revival
Saikat Samanta, Saptarshi Karmakar, Satayajay Behuria, Shibam Dutta, Soujit Das, Soumik Saha
https://arxiv.org/abs/2405.05813

Revitalising Stagecraft: NLP-Driven Sentiment Analysis for Traditional Theater Revival
This paper explores the application of FilmFrenzy, a python based ticket booking web application, in the revival of traditional Indian theatres. Additionally, this research paper explores how NLP can be implemented to improve user experience. Through clarifying audience views and pinpointing opportunities for development, FilmFrenzy aims to promote involvement and rejuvenation in India's conventional theatre scene. The platform seeks to maintain the relevance and vitality of conventional theatr…

Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination
Soojong Kim, Kwanho Kim, Claire Wonjeong Jo
https://arxiv.org/abs/2404.06731

Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination
Objective. Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT, a widely used and freely available service built upon an LLM, for sentiment analysis to discern different stances toward Human Papillomav…

TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis
Ming Zhou, Weize Quan, Ziqi Zhou, Kai Wang, Tong Wang, Dong-Ming Yan
https://arxiv.org/abs/2404.04545

TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment by leveraging language, visual, and acoustic modalities. Despite the remarkable performance exhibited by previous MSA approaches, the presence of inherent multimodal heterogeneities poses a challenge, with the contribution of different modalities varying considerably. Past research predominantly focused on improving representation learning techniques and feature fusion strategies. However, many of these efforts overlook…

Finding fake reviews in e-commerce platforms by using hybrid algorithms
Mathivanan Periasamy, Rohith Mahadevan, Bagiya Lakshmi S, Raja CSP Raman, Hasan Kumar S, Jasper Jessiman
https://arxiv.org/abs/2404.06339

Finding fake reviews in e-commerce platforms by using hybrid algorithms
Sentiment analysis, a vital component in natural language processing, plays a crucial role in understanding the underlying emotions and opinions expressed in textual data. In this paper, we propose an innovative ensemble approach for sentiment analysis for finding fake reviews that amalgamate the predictive capabilities of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers. Our ensemble architecture strategically combines these diverse models to capitalize on…

💪 Relational Graph Convolutional Networks for Sentiment Analysis
#computerscience

Relational Graph Convolutional Networks for Sentiment Analysis
With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which offer interpretability and flexibility by capturing dependencies between data points represented…

Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis
Yuchen Du, Tho V. Le
https://arxiv.org/abs/2405.00688

Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis
This article presents a comprehensive sentiment analysis (SA) of comments on YouTube videos related to Sidewalk Delivery Robots (SDRs). We manually annotated the collected YouTube comments with three sentiment labels: negative (0), positive (1), and neutral (2). We then constructed models for text sentiment classification and tested the models' performance on both binary and ternary classification tasks in terms of accuracy, precision, recall, and F1 score. Our results indicate that, in binary …

Joint sentiment analysis of lyrics and audio in music
Lea Schaab, Anna Kruspe
https://arxiv.org/abs/2405.01988 https://arxiv.org/pdf/…

Joint sentiment analysis of lyrics and audio in music
Sentiment or mood can express themselves on various levels in music. In automatic analysis, the actual audio data is usually analyzed, but the lyrics can also play a crucial role in the perception of moods. We first evaluate various models for sentiment analysis based on lyrics and audio separately. The corresponding approaches already show satisfactory results, but they also exhibit weaknesses, the causes of which we examine in more detail. Furthermore, different approaches to combining the au…

Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
Walid Hariri
https://arxiv.org/abs/2404.01800

Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
Scientific articles play a crucial role in advancing knowledge and informing research directions. One key aspect of evaluating scientific articles is the analysis of citations, which provides insights into the impact and reception of the cited works. This article introduces the innovative use of large language models, particularly ChatGPT, for comprehensive sentiment analysis of citations within scientific articles. By leveraging advanced natural language processing (NLP) techniques, ChatGPT ca…

This https://arxiv.org/abs/2309.13297 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review. It aims to analyze user-generated reviews to determine a) the target entity being reviewed, b) the high-level aspect to which it belongs, c) the sentiment words used to express the opinion, and d) the sentiment expressed toward the targets and the aspects. While various benchmark datasets have fostered advancements in ABSA, they often come with domain limitat…

Thread review sentimental analysis with tkinter GUI & tableau dashboard
Robin Donal
https://arxiv.org/abs/2405.00377 https://arxi…

Thread review sentimental analysis with tkinter GUI & tableau dashboard
This project focuses on utilizing a combination of Tkinter for GUI development and Tableauf for data visualization to do sentiment analysis on thread reviews.The main goal is to evaluate and visualize consumer sentiments as they are expressed in thread reviews in order to provide insights into areas for improvement, preferences, and customer satisfaction.The procedure starts with gathering thread reviews from many sources, which are then cleaned and prepared for analysis through preprocessing.S…

Maite Taboada is giving the first keynote at #DGfS2024
She’s starting by giving an introduction to sentiment analysis #nlproc #linguistics

This https://arxiv.org/abs/2402.07787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) evaluates sentiment expressions within a text to comprehend sentiment information. Previous studies integrated external knowledge, such as knowledge graphs, to enhance the semantic features in ABSA models. Recent research has examined the use of Graph Neural Networks (GNNs) on dependency and constituent trees for syntactic analysis. With the ongoing development of ABSA, more innovative linguistic and structural features are being incorporated (e.g. latent …

This https://arxiv.org/abs/2402.10481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Emoji Driven Crypto Assets Market Reactions
In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators like BTC Price and the VCRIX index. Our architectur…

Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
Wei Xu, Jianlong Chen, Zhicheng Ding, Jinyin Wang
https://arxiv.org/abs/2404.17123

Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model. The study firstly analyses the word cloud model of the text with six sentiment labels, and then carries out data preprocessing, including the steps of removing special symbols, punctuation marks, numbers, stop words and non-alphabetic parts.…

LLMs for Targeted Sentiment in News Headlines: Exploring Different Levels of Prompt Prescriptiveness
Jana Juro\v{s}, Laura Majer, Jan \v{S}najder
https://arxiv.org/abs/2403.00418 …

LLMs for Targeted Sentiment in News Headlines: Exploring Different Levels of Prompt Prescriptiveness
News headlines often evoke sentiment by intentionally portraying entities in particular ways, making targeted sentiment analysis (TSA) of headlines a worthwhile but difficult task. Fine-tuned encoder models show satisfactory TSA performance, but their background knowledge is limited, and they require a labeled dataset. LLMs offer a potentially universal solution for TSA due to their broad linguistic and world knowledge along with in-context learning abilities, yet their performance is heavily i…

Investigating the dissemination of STEM content on social media with computational tools
Oluwamayokun Oshinowo, Priscila Delgado, Meredith Fay, C. Alessandra Luna, Anjana Dissanayaka, Rebecca Jeltuhin, David R. Myers
https://arxiv.org/abs/2404.18944

Investigating the dissemination of STEM content on social media with computational tools
Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer c…

Exploring and Applying Audio-Based Sentiment Analysis in Music
Etash Jhanji
https://arxiv.org/abs/2403.17379 https://arxiv.org/pdf/24…

Exploring and Applying Audio-Based Sentiment Analysis in Music
Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text as deeply as they do in music. The ability of a computational model to interpret musical emotions is largely unexplored and could have implications and uses in therapy and musica…

Does anyone know of some approach that does some e.g. sentiment analysis to classify citations as agreement/disagreement?
Obviously: the goal would be to classify the bibliographical data of a work.
Any directions/hints? :)

HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis
Zhuojia Wu, Qi Zhang, Duoqian Miao, Kun Yi, Wei Fan, Liang Hu
https://arxiv.org/abs/2404.11938

HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multimodal data, such as voiceprints and facial images. Recent distributed collaborative learning has been verified as an effective paradigm for privacy preservation in multimodal tasks. However, they often overlook the privacy distinctions among different modalities, struggling to strike a balance between performance and p…

Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
Zhenxiao Cheng, Jie Zhou, Wen Wu, Qin Chen, Liang He
https://arxiv.org/abs/2402.18145

Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-level importance using dimension-level gradient values through a norm function, often presuming equal significance for all gradient dimensions. However, in the context of Aspect-based Sentiment Analysis (ABSA), our preliminary research suggests that only specific dimensions are pertinent. To address this, we propose the …

Exploring and Applying Audio-Based Sentiment Analysis in Music
Etash Jhanji
https://arxiv.org/abs/2403.17379 https://arxiv.org/pdf/24…

Exploring and Applying Audio-Based Sentiment Analysis in Music
Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text as deeply as they do in music. The ability of a computational model to interpret musical emotions is largely unexplored and could have implications and uses in therapy and musica…

Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Siyin Wang, Jie Zhou, Qin Chen, Qi Zhang, Tao Gui, Xuanjing Huang
https://arxiv.org/abs/2402.14536 …

Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain. Whereas, most methods are proposed under the assumption that the target (test) domain is known, making them fail to generalize well on unknown test data that is not always available in practice. In this paper, we focus on the problem of domain generalization for cross-domain sentiment analysis. Specifically, we propose a backdoor adjustment-based causal …

Does anyone know of some approach that does some e.g. sentiment analysis to classify citations as agreement/disagreement?
Obviously: the goal would be to classify the bibliographical data of a work.
Any directions/hints? :)

This https://arxiv.org/abs/2403.04639 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis
The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further, we also provide a linguistics analysis of the dataset to understand the structure of code-mixing and a statistical study to understand the language preferences of speakers with different polarities. With these analyses, we also train …

Internet sentiment exacerbates intraday overtrading, evidence from A-Share market
Peng Yifeng
https://arxiv.org/abs/2404.12001 https://

Internet sentiment exacerbates intraday overtrading, evidence from A-Share market
Market fluctuations caused by overtrading are important components of systemic market risk. This study examines the effect of investor sentiment on intraday overtrading activities in the Chinese A-share market. Employing high-frequency sentiment indices inferred from social media posts on the Eastmoney forum Guba, the research focuses on constituents of the CSI 300 and CSI 500 indices over a period from 01/01/2018, to 12/30/2022. The empirical analysis indicates that investor sentiment exerts a…

FNSPID: A Comprehensive Financial News Dataset in Time Series
Zihan Dong, Xinyu Fan, Zhiyuan Peng
https://arxiv.org/abs/2402.06698 https://

FNSPID: A Comprehensive Financial News Dataset in Time Series
Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in large language models motivate the integrated financial analysis of both sentiment data, particularly market news, and numerical factors. Nonetheless, this methodology frequentl…

This https://arxiv.org/abs/2404.03259 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enhancing the Performance of Aspect-Based Sentiment Analysis Systems
Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity. While Graph Convolutional Networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance. Specifically,we first integrate …

This https://arxiv.org/abs/2404.12001 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Internet sentiment exacerbates intraday overtrading, evidence from A-Share market
Market fluctuations caused by overtrading are important components of systemic market risk. This study examines the effect of investor sentiment on intraday overtrading activities in the Chinese A-share market. Employing high-frequency sentiment indices inferred from social media posts on the Eastmoney forum Guba, the research focuses on constituents of the CSI 300 and CSI 500 indices over a period from 01/01/2018, to 12/30/2022. The empirical analysis indicates that investor sentiment exerts a…

This https://arxiv.org/abs/2311.07978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

How good are Large Language Models on African Languages?
Recent advancements in natural language processing have led to the proliferation of large language models (LLMs). These models have been shown to yield good performance, using in-context learning, even on tasks and languages they are not trained on. However, their performance on African languages is largely understudied relative to high-resource languages. We present an analysis of four popular large language models (mT0, Aya, LLaMa 2, and GPT-4) on six tasks (topic classification, sentiment cl…

Emoji Driven Crypto Assets Market Reactions
Xiaorui Zuo, Yao-Tsung Chen, Wolfgang Karl H\"ardle
https://arxiv.org/abs/2402.10481 https://

Emoji Driven Crypto Assets Market Reactions
In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators like BTC Price and the VCRIX index. This approach m…

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu
https://arxiv.org/abs/2403.07342

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive learning approach to mitigate these challenges. The proposed approach demonstrates comparable or superior performance in comparison to state…

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu
https://arxiv.org/abs/2403.07342

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive learning approach to mitigate these challenges. The proposed approach demonstrates comparable or superior performance in comparison to state…

LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey
Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh, Bala Mallikarjunarao Garlapati, Srinivasa Rao Chalamala, Rahul Mishra
https://arxiv.org/abs/2402.14558

LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey
Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenu…

Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard
Ariel Rosenfeld, Teddy Lazebnik
https://arxiv.org/abs/2402.14533

Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard
Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality. However, it is unclear whether LLMs tend to exhibit distinctive linguistic styles akin to how human authors do. Through a comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech (POS) distribution, dependency distribution, and sentiment of texts generated by three of the most popular LLMS today (GPT-3.5, GPT-4, and Bard) to diverse inputs. The results point to signific…

This https://arxiv.org/abs/2306.12951 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations
ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generation tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we investigated public attitudes towards ChatGPT by applying natural language processing techniques such as sentiment analysis and topic modeling to Twitter data from December 5, 2022 to June 10, 2023…