A Personalized Sentiment Model with Textual and Contextual Information

Guo, Shize; Höhn, Sviatlana; Schommer, Christoph

doi:10.18653/v1/k19-1093

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…temporal point processes such as the Hawkes mechanism have shown great promise in modeling social media dynamics (Rizoiu et al, 2017) and user behavior over time (Guo et al, 2019), revalidating the effectiveness of our proposed HEAT mechanism for learning user representations. Further, we note that Hyper-SOS's performance saturates on adding history beyond a year (Figure 6b).…”

Section: Impact Of Historical Context Aggregationmentioning

confidence: 84%

“…To model historical emotions of a user and factor in the natural irregularities in posting time of historical tweets (Lei et al, 2018;Wojcik and Hughes, 2019), we propose the HEAT mechanism: Hawkes temporal Emotion AggregraTion. HEAT leverages Hawkes Process (Hawkes, 1971), a selfexciting temporal point process to model the in-tensity of emotions whenever a tweet is posted in the past (Guo et al, 2019). Intuitively, it assumes that emotions exhibited in different historic tweets can influence one another.…”

Section: Modeling Personal Historical Contextmentioning

confidence: 99%

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Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

Sawhney

Joshi

Shah

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user's emotional history and social information from a user's neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a timesensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.

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Section: Impact Of Historical Context Aggregationmentioning

confidence: 84%

Section: Modeling Personal Historical Contextmentioning

confidence: 99%

Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

Sawhney

Joshi

Shah

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…4 For example, a title keyword search for 'personali' or 'personaliz' returns 124 articles from the ACL Anthology and a further 10 from the arXiv Computation and Language (cs.CL) subclass. These systems cover a wide range of tasks including dialogue [127,157,36,39,41,109,133,146,149,206,238,244], recipe or diet generation [147,87,159], summarisation [215,240], machine translation [156,153,194,237], QA [137,193], search and information retrieval [4,40,59,70,245], sentiment analysis [80,155,226], domain classification [129,114,113], entity resolution [132], and aggression or abuse detection [107,108]; and are applied to a number of societal domains such as education [118,163,241], medicine [3,15,225,235] and news consumption…”

Section: From Implicit To Explicit Personalisationmentioning

confidence: 99%

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Kirk¹,

Vidgen²,

Röttger³

et al. 2023

Preprint

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Large language models (LLMs) are used to generate content for an increasingly wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interfaces like ChatGPT or search engines like Bing. This intensifies the need to ensure that models are aligned with human preferences and do not produce unsafe, inaccurate or toxic outputs. While alignment techniques like reinforcement learning with human feedback (RLHF) and red-teaming can mitigate some safety concerns and improve model capabilities, it is unlikely that an aggregate fine-tuning process can adequately represent the full range of users' preferences and values. Different people may legitimately disagree on their preferences for language and conversational norms, as well as on values or ideologies which guide their communication. Personalising LLMs through micro-level preference learning processes may result in models that are better aligned with each user. However, there are several normative challenges in defining the bounds of a societally-acceptable and safe degree of personalisation. In this paper, we ask how, and in what ways, LLMs should be personalised. First, we review literature on current paradigms for aligning LLMs with human feedback, and identify issues including (i) a lack of clarity regarding what alignment means; (ii) a tendency of technology providers to prescribe definitions of inherently subjective preferences and values; and (iii) a "tyranny of the crowdworker", exacerbated by a lack of documentation in who we are really aligning to. Second, we present a taxonomy of benefits and risks associated with personalised LLMs, for individuals and society at large. Finally, we propose a three-tiered policy framework that allows users to experience the benefits of personalised alignment, while restraining unsafe and undesirable LLM-behaviours within (supra-)national and organisational bounds.

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Learning Personal Human Biases and Representations for Subjective Tasks in Natural Language Processing

Kocoń

Gruza

Bielaniewicz

et al. 2021

2021 IEEE International Conference on Data Mining (ICDM)

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A Personalized Sentiment Model with Textual and Contextual Information

Cited by 4 publications

References 21 publications

Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Learning Personal Human Biases and Representations for Subjective Tasks in Natural Language Processing

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