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Finding the best way to put media bias research into practice via an annotation app ...
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Are neural language models sensitive to false belief? A computational study. ...
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Can distributional semantics explain performance on the false belief task? ...
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The influence of animacy on perspective-taking and word order during language production ...
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Affect-enhancing speech characteristics - the influence of verbal and prosodic components ...
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Comment les animaux et les robots transforment les relations ?
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In: Colloque international « Objets animés, humains, animaux : partenaires de soins tendres » ; https://hal.archives-ouvertes.fr/hal-03504819 ; Colloque international « Objets animés, humains, animaux : partenaires de soins tendres », Nov 2021, Grenoble, France (2021)
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Human communication and robotics
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In: VIHAR Vocal Interactivity in-and-between Humans, Animals and Robots ; https://hal.archives-ouvertes.fr/hal-03504822 ; VIHAR Vocal Interactivity in-and-between Humans, Animals and Robots, Oct 2021, Paris, France (2021)
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Measuring Equity Mindsets and Improvisational Practices Through Language Patterns in Equity Simulations ...
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Using narrative to manipulate perceived mind and word order during language production ...
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Dynamic Generation of Spatial Referring Expressions for Social Robots
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Social Media Text Analytics: An Application to Prescription Opioids-Related Conversations
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In: Graduate Doctoral Dissertations (2021)
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Abstract:
Accurately discovering knowledge from a huge volume of short messages generated daily on social media platforms is a critical challenge. Conventional topic models like Latent Dirichlet Allocation (LDA) and its variants that are widely used to automatically extract thematic information from regular-sized documents fail to discover essential information from short texts. Short text documents such as tweets, compared to regular-sized documents such as news articles, lack word co-occurrence information, which leads to very sparse and high dimensional vector representations. This extreme sparsity brings challenges to applying the conventional topic models on social media short texts. In this study, a novel heuristic topic model denoted as the Hashtag-Cluster-based Aggregation model (HCA) is developed to address this sparseness problem. This heuristic topic model treats tweets as semi-structured texts and uses hashtag relations to aggregate related tweets and create larger text documents for topic modeling. At an application level, the HCA model is used to study the topic of public conversations on Twitter about prescription opioids and joint discussions about prescription opioids and marijuana. Monitoring the topic of these discussions at a population scale, such as among the population of Twitter users, can help policymakers and public health officials to better understand the public perception about prescription opioids, study the association between prescription opioids and marijuana, detect abuse behaviors, and surveil the trend of related incidents. The proposed HCA model is evaluated using TF-IDF, GloVe, and FastText word embeddings in comparison to the Hashtag-based Aggregation model (HA), the most common heuristic hashtag-based Twitter aggregation model in the literature. Findings of the model evaluation proved the impact of including the hashtag information in topic modeling by generating more coherent topics than the HA model, which is based on aggregating tweets with the same hashtags.
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Keyword:
Artificial Intelligence and Robotics; Computer Sciences; Hashtag-Cluster-based Aggregation model; Health Information Technology; Latent Dirichlet Allocation; Natural Language Processing; Prescription Opioids; Public Health; Social Media Analytics; Topic Modeling
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URL: https://scholarworks.umb.edu/cgi/viewcontent.cgi?article=1667&context=doctoral_dissertations
https://scholarworks.umb.edu/doctoral_dissertations/668
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Social Measurement and Causal Inference with Text
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In: Doctoral Dissertations (2021)
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Dimensions of anthropomorphism: From humanness to humanlikeness
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Communicating with robots: What we do wrong and what we do right in artificial social intelligence, and what we need to do better
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An integrative platform to capture the orchestration of gesture and speech
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In: GeSpIn 2019 - Gesture and Speech in Interaction ; https://hal.inria.fr/hal-02278345 ; GeSpIn 2019 - Gesture and Speech in Interaction, Sep 2019, Paderborn, Germany (2019)
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Human, robot an ambient system collaboration for behavior evaluation ; Collaboration entre un humain, un robot et un système ambiant pour l’évaluation de comportements
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In: https://tel.archives-ouvertes.fr/tel-02448718 ; Robotique [cs.RO]. Université de Technologie de Compiègne, 2019. Français. ⟨NNT : 2019COMP2484⟩ (2019)
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"A Stubborn Child" - How Robot Sounds are Oriented to in Everyday Situated Interaction at Home ...
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