Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA.

The global impact of the COVID-19 pandemic has highlighted the need for a comprehensive understanding of public sentiment and reactions. Despite the availability of numerous public datasets on COVID-19, some reaching volumes of up to 100 billion data points, challenges persist regarding the availability of labeled data and the presence of coarse-grained or inappropriate sentiment labels. In this paper, we introduce SenWave, a novel fine-grained multi-language sentiment analysis dataset specifically designed for analyzing COVID-19 tweets, featuring ten sentiment categories across five languages. The dataset comprises 10,000 annotated tweets each in English and Arabic, along with 30,000 translated tweets in Spanish, French, and Italian, derived from English tweets. Additionally, it includes over 105 million unlabeled tweets collected during various COVID-19 waves. To enable accurate fine-grained sentiment classification, we fine-tuned pre-trained transformer-based language models using the labeled tweets. Our study provides an in-depth analysis of the evolving emotional landscape across languages, countries, and topics, revealing significant insights over time. Furthermore, we assess the compatibility of our dataset with ChatGPT, demonstrating its robustness and versatility in various applications. Our dataset and accompanying code are publicly accessible on the repository\footnote{https://github.com/gitdevqiang/SenWave}. We anticipate that this work will foster further exploration into fine-grained sentiment analysis for complex events within the NLP community, promoting more nuanced understanding and research innovations.

Abstract: This paper aims to explore two compet ing data science meth odologies to attempt answer ing th e question, " Which issues contributed most to voters' choice in the 2024 presidential election? " The methodologies involve novel empirical evidence driven by artificial intelligence (AI) techniques . By using two distinct methods based on natural language processing and clustering analysis to mine over eight thousand user comments on election - related YouTube videos from one right leaning journal, Wall Street Journal, and one left leaning journal, New York Times, during pre - election week, we quantify the frequency of selected issue areas among user comments to infer which issues were most salient to potential voters in the seven days preceding the November 5th election. Empirically, we primarily demonstrate that immigration and democracy were the most frequently and consistently invoked issues in user comments on the analyzed YouTube videos, followed by the issue of identity politics, while inflation was significantly less frequently referenced. These results corroborate certain findings of post - election surveys but also refute the supposed importance of inflation as an election issue. This indicate s that variations on opinion mining, with their analysis of raw user data online, ca n be more revealing than polling and surveys for analyzing election outcomes. Keywords: artificial intelligence; opinion mining; clustering; vot e choice; cleavages 1. Introduction The Democrats lost both houses of Congress and the Presidency to Republicans in the 2024 election, with former president Donald Trump winning all seven swing states and the national popular vote, despite most pre - election polls giving Vice President Kamala Harris and President Trump a roughly equal chance of winning . Most post - election punditry and analysis in the legacy press and alternative media has attributed the Democrats' large loss to two main issues - inflation [59] and immigration [30] However, a growing contingent of analysts has also attributed the election outcome to the Democratic party's association with cultural issues purportedly distant from the median voter's preferences, such as th ose alternatively aggregated under the concept of "identity" or " woke " politics [54, 56] . To this point, three post - election studies illustrate how voters associated Democrats with left - of - center ideas that were ostensibly distant from most voters' priorities. S urvey research from the think tank Third Way demonstrates that Democrats, and thus Kamala Harris, were largely perceived as "too liberal" [15], while a study from More In Common polling over 5, 000 Americans concluded that while inflation was the top concern for every major demographic group across both parties, Americans misperceived LGBT/transgender policies as the top policy priority for Democrats [37] .

However, the representation capability of existing deep topic models is still limited by the phenomenon of " posterior collapse ", which has been widely

Nonetheless, this framework has two main limitations. First, A TM relies on the key ingredient: leveraging the discrimination of the real distribution from the fake (negative) distribution to guide the training.

We wish to thank all of the reviewers for their time and thorough reading of our paper! We appreciate the reviewer's suggestions regarding clarity. We have added the suggested summary sentence "the key We started with binary sentiment classification, but are actively working on more tasks. RNN hidden states onto the top two PCs for two different input sequences that differ only by two tokens (replacing ' The trajectories start out the same as the initial tokens are identical. We have added a footnote noting this in the main text.

However, these models inevitably face two potential problems.

Spoken dialogue systems (SDSs) utilize automatic speech recognition (ASR) at the front end of their pipeline. The role of ASR in SDSs is to recognize information in user speech related to response generation appropriately. Examining selective listening of humans, which refers to the ability to focus on and listen to important parts of a conversation during the speech, will enable us to identify the ASR capabilities required for SDSs and evaluate them. In this study, we experimentally confirmed selective listening when humans generate dialogue responses by comparing human transcriptions for generating dialogue responses and reference transcriptions. Based on our experimental results, we discuss the possibility of a new ASR evaluation method that leverages human selective listening, which can identify the gap between transcription ability between ASR systems and humans.

To tackle the limitations, we introduce SpokenWOZ, a large-scale speech-text dataset for spoken TOD, containing 8 domains, 203k turns, 5.7k dialogues and 249 hours of audios from human-to-human spoken