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Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media 23

Leveraging LLMs for Integrated Sentiment and Topic Analysis ...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sibitenda, Harriet Hu, Ruofan Rundensteiner, Elke Diattara, Awa Traore, Assitan Ba, Cheikh University of Gaston Berger Computer Science Department Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester United States Data Science Program Worcester Polytechnic Institute Worcester United States Actroll Data Science Department France

ISBN: (纸本)9798350374889

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with 'Poverty' achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions. © 2024 IEEE.

关键词： Tweets

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An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis 25

An Integrated Framework for Team Formation and Winner Predic...

引用

25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Galbiati, Federico Gran, Ranier X. Jacques, Brendan D. Mulhern, Sullivan J. Ngan, Chun-Kit Worcester Polytechnic Institute Department of Computer Science Worcester United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350330014

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy. © 2023 IEEE.

关键词： Predictive analytics

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Swarm Intelligence Path-Planning Pipeline and Algorithms for UAVs: Simulation, Analysis and Recommendation 26

Swarm Intelligence Path-Planning Pipeline and Algorithms for...

引用

26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Harris, Wyatt Tseng, Sean Viso, Tabatha Weissman, Max Ngan, Chun-Kit Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States

ISBN: (纸本)9789897586927

This research work aims to support domain experts in the selection of proper path planning algorithms for UAVs to solve a domain business problem (i.e., the last mile delivery of goods). In-depth analysis, insight, and recommendations of three promising approaches, including reinforcement learning-based, bio-inspired-based, and physics-based are used to address the multi-agent UAV path planning problem. Specifically, the contributions of this work are fourfold: First, we develop a unified pipeline to implement each approach to conduct this analysis. Second, we build a 2D UAV path planning environment to simulate each approach. Third, using this 2D environment, we run the 450 simulations in three different group sizes of swarm UAV agents (i.e., 3, 5, and 10) within three environments of varying complexity (i.e., Easy, Intermediate, and Hard). We aggregate the simulation data and compare their performance in terms of success rate, run-time, and path length while using the classical A* Search as a baseline. Finally, based upon the performance of each approach and our analytical investigations, we provide informed recommendations for the optimal use case of each UAV path planning approach. The recommendations are presented using parameters for environmental complexity and urgency of goods delivery. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Motion planning

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Exploring Neural Network Structure through Sparse Recurrent Neural Networks: A Recasting and Distillation of Neural Network Hyperparameters 22

Exploring Neural Network Structure through Sparse Recurrent ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Hershey, Quincy Paffenroth, Randy Pathak, Harsh Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States

ISBN: (纸本)9798350345346

This paper explores the role of sparse parameteri-zations on Recurrent Neural Network (RNN) performance using anomaly detection tasks. The findings indicate sparsity plays a significant role in both improving training stability and overall performance of RNNs. Sparsity of the weight matrix is initially left to vary while the dimensions of the weight matrix remain fixed, causing total trainable weights to vary in response. The sparse RNN models demonstrate surprisingly low performance sensitivity to the resulting changes in total trainable weights. Building on these results, sparsity of the weight matrix is again left to vary with a fixed number of trainable weights and the dimensions of the weight matrix permitted to change with sparsity. By isolating the impact of sparse parameterizations, RNNs show improved training stability and overall performance. Moreover, the results indicate an optimal band of sparsity within which RNNs perform most efficiently across a difficult time-series task. Sparse RNN configurations are compared to LSTM models in anomaly detection tasks. Sparse RNN models demonstrate a strong performance advantage over LSTM models, even out-performing in scenarios where memory is crucial and LSTM models are expected to perform better. The results indicate the reputation of RNN training instability may be exacerbated by overparamaterization resulting from dense configurations. Several characteristics such as model depth and model sparsity are presented which significantly improve the stability and expressiveness of model performance across a wider array of hyperparameters while improving performance differentials at significantly reduced weight counts. Ultimately the paper finds the potential of RNNs to be better realized through sparse parameterizations. © 2023 IEEE.

关键词： recurrent neural networks RNN sparsity

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Sequentia12D: Organizing Center of Skip Connections for Transformers 22

Sequentia12D: Organizing Center of Skip Connections for Tran...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Pathak, Harsh Nilesh Paffenroth, Randy Hershey, Quincy Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States

ISBN: (纸本)9798350345346

Skip connections (SCs) are commonly employed in neural networks to facilitate gradient-based training and often lead to improved performance in deep learning. To implement SCs, a user writes custom modules along with *** which limits the extensibility of neural network design with SC. Despite their versatility and user-friendliness, there is an opportunity to create a standardized approach for organizing SCs in various applications. Establishing a clear organizing framework would significantly enhance the widespread adoption of this powerful technique, amplifying its impact. We introduce Sequential2D as an enhanced orchestrator which provides in-sights into not only the width and depth of the network but also a neural network's intra-connections such as SCs. The functionality provided by the Sequential2D architecture is a strict super-set of the functionality found in the common *** network. The 2D in Sequential2D refers to the two-dimensional matrix of functions, as opposed to the one-dimensional cascading array of functions in a Pytorch or Keras sequential container. As an example of our proposed approach, we demonstrate the effectiveness of our Sequential2D architecture by substantially improving the fine-tuning performance of the GPT-2 model with less than 1% additional parameters. © 2023 IEEE.

关键词： deep learning GPT-2 Neural Network nlp recurrent neural networks RNN Sequential skip-connection sparsity transformer

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DeepScreen: Boosting Depression Screening Performance with an Auxiliary Task

DeepScreen: Boosting Depression Screening Performance with a...

引用

2023 IEEE International Conference on Big data, Bigdata 2023

作者： Flores, Ricardo Shrestha, Avantika Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science WorcesterMA United States Worcester Polytechnic Institute Computer Science/Data Science WorcesterMA United States

ISBN: (纸本)9798350324457

Depression is a prevalent mental health condition with severe impacts on physical and social health. It is costly and difficult to detect, requiring substantial time from trained mental professionals. To alleviate this burden, recent research explores the diagnostic capabilities of deep learning models trained on modalities extracted from videos of clinical interviews for depression screening. However, training deep learning models is challenging because in the mental health domain datasets contain a small number of patients. To address this challenge, we propose DeepScreen, a recurrent deep-learning architecture for depression screening whose performance is boosted by deploying a self-supervised auxiliary task for selective missing value imputation. DeepScreen leverages a multi-task architecture with a bidirectional recurrent deep learning model and a self-attention mechanism, jointly optimizing the supervised depression and the self-supervised auxiliary task. Our first study assesses the capability of the auxiliary task training of DeepScreen under different correlation levels and masking sub-sequence sizes of multi-variate time series. Found to be effective, our second study evaluates DeepScreen on 15 data sets composed of real-world temporal facial landmark features extracted from responses to different clinical interview questions. The results achieved across all 15 datasets demonstrate that the imputation task boosts the depression prediction metrics significantly. Specifically, DeepScreen improves the F1 score for one of the datasets by 57%. Further, our best-performing model achieves an F1 score of 0.85. This work provides valuable insights into improving deep learning driven mental screening applications by leveraging auxiliary tasks such as imputation for learning a better representation even from small datasets. © 2023 IEEE.

关键词： Digital health missing value imputation multivariate time series sequential modeling for mental health screen

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Conditioned Cycles in Sparse data Domains: Applications to Electromagnetics 22

Conditioned Cycles in Sparse Data Domains: Applications to E...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Barger, Maria Paffenroth, Randy Pathak, Harsh Worcester Polytechnic Institute Data Science WorcesterMA United States Worcester Polytechnic Institute Mathematics Computer Science and Data Science WorcesterMA United States

ISBN: (纸本)9798350345346

Training neural networks is difficult with small datasets, yet small training data sets are commonly found with machine learning problems in the physical sciences. Prediction accuracy within such sparse data domains can be improved by the utilization of conditioning, inspired by classical statistics, and cycles, similar to those of CycleGANs. In this paper, we propose the addition of 'oracle' conditioning, where the true condition value is provided during training (but not during testing), to improve final prediction accuracy in sparse data domains. For example, in over-water electromagnetic (EM) source localization, the range of the emitter can be thought of as a 'condition' on the generation of a received EM signal. Oracle conditioning utilizes an encoder-decoder framework where the conditioning of the decoder is not contingent on the output of an encoder's prediction (e.g, predicting the range from a received EM signal). While our primary application involves source localization for EM signals, this paper is intended to inspire a general approach toward neural network training for problems in the physical sciences with similar characteristics. Our multi-component loss function is intended to serve as a general method for small data problems which can benefit from the addition of cycles and oracle conditioning. © 2023 IEEE.

关键词： conditional generation cycles electromagnetics loss function oracle conditioning

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FSL-LFMG: Few-Shot Learning with Augmented Latent Features and Multitasking Generation for Enhancing Multiclass Classification on Tabular data 16th

FSL-LFMG: Few-Shot Learning with Augmented Latent Features a...

引用

16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Nur, Aviv A. Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute 100 Institute Rd WorcesterMA United States thyssenkrupp Materials Services GmbH Essen Germany

ISBN: (纸本)9789897587214

In this work, we propose advancing ProtoNet that employs augmented latent features (LF) by an autoencoder and multitasking generation (MG) by STUNT in the few-shot learning (FSL) mechanism. Specifically, the achieved contributions to this work are threefold. First, we propose an FSL-LFMG framework to develop an end-to-end few-shot multiclass classification workflow on tabular data. This framework is composed of three main stages that include (i) data augmentation at the sample level utilizing autoencoders to generate augmented LF, (ii) data augmentation at the task level involving self-generating multitasks using the STUNT approach, and (iii) the learning process taking place on ProtoNet, followed by various model evaluations in our FSL mechanism. Second, due to the outlier and noise sensitivity of K-means clustering and the curse of dimensionality of Euclidean distance, we enhance and customize the STUNT approach by using K-medoids clustering that is less sensitive to noisy outliers and Manhattan distance that is the most preferable for highdimensional data. Finally, we conduct an extensive experimental study on four diverse domain datasets—Net Promoter Score segmentation, Dry Bean type, Wine type, and Forest Cover type—to prove that our FSLLFMG approach on the multiclass classification outperforms the Tree Ensemble models and the One-vs-therest classifiers by 7.8% in 1-shot and 2.5% in 5-shot learning. © 2024 by Paper published under CC license (CC BY-NC-ND 4.0)

关键词： Decision trees

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting 19

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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19th IEEE International Conference on Automation science and Engineering, CASE 2023

作者： Akl, James Kodate, Shreedhar Calli, Berk Worcester Polytechnic Institute Robotics Engineering Department WorcesterMA01609 United States Worcester Polytechnic Institute Data Science Program WorcesterMA01609 United States

ISBN: (纸本)9798350320695

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our 'Oxy-fuel Cutting Task State' (OCTS) dataset is available at ***/10.5281/zenodo.7734951. © 2023 IEEE.

关键词： Risk assessment

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LLM-based Hierarchical Label Annotation for Foodborne Illness Detection on Social Media

LLM-based Hierarchical Label Annotation for Foodborne Illnes...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Zhang, Dongyu Hu, Ruofan Tao, Dandan Feng, Hao Rundensteiner, Elke Aml ByteDance San Jose United States Worcester Polytechnic Institute United States Worcester Polytechnic Institute Data Science Program Worcester United States Tsinghua University Vanke School of Public Health Beijing China North Carolina A&t State University College of Ag&Environ Sciences Greensboro United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350362480

Foodborne illnesses pose a threat to public health, leading to morbidity, mortality, and economic burden annually. Social media, while providing a rich timely source for training AI models for surveillance, requires effective tools for annotation. While Large Language Models (LLMs) have shown promise for generating simple labels, here hierarchical labels composed of entity types like food type and symptom (at individual word level) and the foodborne illness event (at complete post level) are required. For this, we introduce ICL2FID, the first LLM-based hierarchical labeling framework designed to annotate social media posts for foodborne illness detection at two levels using only a few demonstration examples. To utilize the interconnection between post and word levels, ICL2FID instructs the LLM to leverage information from one level when predicting the other level. To combat model hallucination and cyclic dependencies, a verification step improves evidence propagation between interconnected word and post-level labeling tasks. Strategies for custom selection of demonstration examples are designed reducing biases and increasing representation. We compare ICL2FID against traditional supervised learning and other LLM methods, demonstrating that it not only achieves superior accuracy but does so at a fraction of the cost and time. These findings highlight ICL2FID's potential as a viable alternative for hierarchical label generation in scenarios with limited resources and huge data sets. Code is available at https://***/zdy93/ICL2FID. © 2024 IEEE.

关键词： Demonstrations

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