The U.K.'s privacy watchdog has warned against use of so-called "emotion analysis" technologies for anything more serious than kids' party games, saying there's a discrimination risk attached to applying "immature" biometric tech that makes pseudoscientific claims about being able to recognize people's emotions using AI to interpret biometric data inputs. Such AI systems'function', if we can use the word, by claiming to be able to'read the tea leaves' of one or more biometric signals, such as heart rate, eye movements, facial expression, skin moisture, gait tracking, vocal tone etc, and perform emotion detection or sentiment analysis to predict how the person is feeling -- presumably after being trained on a bunch of visual data of faces frowning, faces smiling etc (but you can immediately see the problem with trying to assign individual facial expressions to absolute emotional states -- because no two people, and often no two emotional states, are the same; hence hello pseudoscience!). The watchdog's deputy commissioner, Stephen Bonner, appears to agree that this high tech nonsense must be stopped -- saying today there's no evidence that such technologies do actually work as claimed (or that they will ever work). "Developments in the biometrics and emotion AI market are immature. They may not work yet, or indeed ever," he warned in a statement. "While there are opportunities present, the risks are currently greater.

This paper shows to what extent machine learning can analyze and structure these databases. An automated data analysis pipeline is deployed to provide insights into user-generated content for researchers in other domains. First, the domain expert can select an image and a term of interest. Then, the pipeline uses image retrieval to find all images showing similar contents and applies aspect-based sentiment analysis to outline users' opinions about the selected term. As part of an interdisciplinary project between architecture and computer science researchers, an empirical study of Hamburg's Elbphilharmonie was conveyed on 300 thousand posts from the platform Flickr with the hashtag'hamburg'. Image retrieval methods generated a subset of slightly more than 1.5 thousand images displaying the Elbphilharmonie. We found that these posts mainly convey a neutral or positive sentiment towards it. With this pipeline, we suggest a new big data analysis method that offers new insights into end-users opinions, e.g., for architecture domain experts.

ABSTRACT Micro-mobility is promising to contribute to sustainable cities in the future with its efficiency and low cost. To better design such a sustainable future, it is necessary to understand the trends and challenges. Thus, we examined people's opinions on micro-mobility in the US and the EU using Tweets. We used topic modeling based on advanced natural language processing techniques and categorized the data into seven topics: promotion and service, mobility, technical features, acceptance, recreation, infrastructure and regulations. Furthermore, using sentiment analysis, we investigated people's positive and negative attitudes towards specific aspects of these topics and compared the patterns of the trends and challenges in the US and the EU. We found that 1) promotion and service included the majority of Twitter discussions in the both regions, 2) the EU had more positive opinions than the US, 3) micro-mobility devices were more widely used for utilitarian mobility and recreational purposes in the EU than in the US, and 4) compared to the EU, people in the US had many more concerns related to infrastructure and regulation issues. These findings help us understand the trends and challenges and prioritize different aspects in micro-mobility to improve their safety and experience across the two areas for designing a more sustainable micro-mobility future. INTRODUCTION The growth of transportation has raised the need for compact, flexible, and more sustainable forms of transportation. Recent developments in the micro-mobility industry show that these devices might address this issue and offer people safer and cheaper trips with reduced travel time. According to the Society of Automotive Engineers (SAE) definition (Society of Automotive Engineers, 2019), micro-mobility refers to a range of small, less than 500 pounds (227 kg) lightweight, fully motorized or motor-assisted devices operating at a speed below 30 mph (48 km/h) and ideal for trips up to 10 km. Typical examples include e-bikes, e-scooters, e-unicycles and e-skateboards, and some of them are widely used as personal or shared transportation devices (Price, Blackshear, Blount Jr, & Sandt, 2021). The global micro-mobility market has been increasing over the years. According to the NACTO (National Association of City Transportation Officials, 2020), 136 million trips were generated by shared micro-mobility in 2019 in the U.S., which was 60% more than 2018. Thus, micro-mobility devices can be well integrated into the overall urban design process of smart and sustainable transportation in the near future. With the sustainable design and development goal, we should not only consider technical challenges and requirements (e.g., battery and material), but also complement and constrain the design and development process by social, infrastructural, and political schemes for a sustainable future (Jiao, Luo, Malmqvist, Johan, & Summers, 2022).

This paper investigates the effectiveness and implementation of modality-specific large-scale pre-trained encoders for multimodal sentiment analysis~(MSA). Although the effectiveness of pre-trained encoders in various fields has been reported, conventional MSA methods employ them for only linguistic modality, and their application has not been investigated. This paper compares the features yielded by large-scale pre-trained encoders with conventional heuristic features. One each of the largest pre-trained encoders publicly available for each modality are used; CLIP-ViT, WavLM, and BERT for visual, acoustic, and linguistic modalities, respectively. Experiments on two datasets reveal that methods with domain-specific pre-trained encoders attain better performance than those with conventional features in both unimodal and multimodal scenarios. We also find it better to use the outputs of the intermediate layers of the encoders than those of the output layer. The codes are available at https://github.com/ando-hub/MSA_Pretrain.

With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual conversational settings. However, most research is natural language processing is focused on monolingual text. To further the work in code-switched sentiment analysis, we propose a multi-step natural language processing algorithm utilizing points of code-switching in mixed text and conduct sentiment analysis around those identified points. The proposed sentiment analysis algorithm uses semantic similarity derived from large pre-trained multilingual models with a handcrafted set of positive and negative words to determine the polarity of code-switched text. The proposed approach outperforms a comparable baseline model by 11.2% for accuracy and 11.64% for F1-score on a Spanish-English dataset. Theoretically, the proposed algorithm can be expanded for sentiment analysis of multiple languages with limited human expertise.

Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a convolutional neural network supplemented with some heuristic rules.

Multimodal representation learning is a challenging task in which previous work mostly focus on either uni-modality pre-training or cross-modality fusion. In fact, we regard modeling multimodal representation as building a skyscraper, where laying stable foundation and designing the main structure are equally essential. The former is like encoding robust uni-modal representation while the later is like integrating interactive information among different modalities, both of which are critical to learning an effective multimodal representation. Recently, contrastive learning has been successfully applied in representation learning, which can be utilized as the pillar of the skyscraper and benefit the model to extract the most important features contained in the multimodal data. In this paper, we propose a novel framework named MultiModal Contrastive Learning (MMCL) for multimodal representation to capture intra- and inter-modality dynamics simultaneously. Specifically, we devise uni-modal contrastive coding with an efficient uni-modal feature augmentation strategy to filter inherent noise contained in acoustic and visual modality and acquire more robust uni-modality representations. Besides, a pseudo siamese network is presented to predict representation across different modalities, which successfully captures cross-modal dynamics. Moreover, we design two contrastive learning tasks, instance- and sentiment-based contrastive learning, to promote the process of prediction and learn more interactive information related to sentiment. Extensive experiments conducted on two public datasets demonstrate that our method surpasses the state-of-the-art methods.

Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition. However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due to the following challenges: (1) Code-switched corpus, unlike monolingual corpus, consists of more than one language and existing methods can't be applied efficiently, (2) Code-switched corpus is usually made of resource-rich and low-resource languages and upon using multilingual pre-trained language models, the final model might bias towards resource-rich language. In this paper, we focus on code-switched sentiment analysis where we have a labelled resource-rich language dataset and unlabelled code-switched data. We propose a framework that takes the distinction between resource-rich and low-resource language into account. Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples. Extensive experiments across multiple language pairs demonstrate that progressive training helps low-resource language dominated samples.

We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zero-shot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets. Our code and datasets are available at https://github.com/cgraywang/IELM

Introducing Deepchecks - Tests for Continuous Validation of ML Models & Data $ pip install deepchecks -U --user Deepchecks is a Python package for comprehensively validating your machine-learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more. While you're in the research phase and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. What Do You Need in Order to Start? Depending on your phase and what you wish to validate, you'll need a subset of the following: Raw data (before pre-processing such as OHE, string processing, etc.), with optional labels The model's training data with labels Test data (which the model isn't exposed to) with labels A supported model that you wish to validate, including: scikit-learn, XGBoost, PyTorch, and more.