Multi-view Story Characterization from Movie Plot Synopses and Reviews

Kar, Sudipta; Aguilar, Gustavo; Lapata, Mirella; Solorio, Thamar

doi:10.18653/v1/2020.emnlp-main.454

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The MovieLens data set, however, is mainly suitable for CF approaches and has limited textual content, which can affect the performance of CB approaches [28]. Thus, all items in the MovieLens data set are further enriched with plot synopses which are available in the movie plot synopses with tags (MPST) data set [29]. MPST is a corpus of ≈70 fine-grained tags and their associations with ≈14 K plot synopses of movies.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

Saat,

Noah,

Mohd

2024

ELEKTRON ELEKTROTECH

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Content-based (CB) recommendation algorithms recommend items to a user based on items the user liked in the past. CB methodologies have gained attention due to their higher accuracy and transparency and the emergence of new technologies, such as knowledge graphs (KGs), advances in natural language processing (NLP), and sentiment analysis. While most previous studies have mainly focussed on the use of term frequency-inverse document frequency (TF-IDF) and other related enhancements, little work can be found on using KGs in CB recommendations. This paper presents an enhancement of the conventional CB recommendation by incorporating KGs for a movie domain. The graph is constructed using the MovieLens data set, which is augmented with additional features such as actors, directors, and genres. Furthermore, the graph is expanded by incorporating topics derived from latent dirichlet allocation (LDA) extraction. Using the KGs, the proposed approach enhances user profiles by leveraging the interconnected user-movie relationship within a graph structure. The results of the experiments showed that the proposed approach exceeded the tested baselines in terms of precision, recall, and F-score metrics.

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Section: Methodsmentioning

confidence: 99%

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

Saat,

Noah,

Mohd

2024

ELEKTRON ELEKTROTECH

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“…In the paper, they tackled the problem of simile generation. Kar et al [2020] tackled story characterization from movie plot synopses and multi-view multi-labeled reviews. This study suggested the importance of considering that the different recipients of the story have their perspectives.…”

Section: How To Evaluate Story-like Textmentioning

confidence: 99%

Computational Storytelling and Emotions: A Survey

Mori¹,

Yamane²,

Mukuta³

et al. 2022

Preprint

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Storytelling has always been vital for human nature. From ancient times, humans have used stories for several objectives including entertainment, advertisement, and education. Various analyses have been conducted by researchers and creators to determine the way of producing good stories. The deep relationship between stories and emotions is a prime example. With the advancement in deep learning technology, computers are expected to understand and generate stories. This survey paper is intended to summarize and further contribute to the development of research being conducted on the relationship between stories and emotions. We believe creativity research is not to replace humans with computers, but to find a way of collaboration between humans and computers to enhance the creativity. With the intention of creating a new intersection between computational storytelling research and human creative writing, we introduced creative techniques used by professional storytellers.

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“…Instead, our distant-supervision methodology leverages information from the conversation level to the utterance level based on a key observa-1 http://www.life1995.org.tw/ tion that the risk of suicidal ideation can often be determined by only a few utterances. Our approach also differs from previous hierarchical and multigrained approaches (Kar et al, 2020;Angelidis and Lapata, 2018) that seek to distill fine-grained information from every smaller piece of the entire input.…”

Section: Introductionmentioning

confidence: 99%

Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

Wang,

Huang,

Hsu

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller's suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01%. In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94% compared to the baseline models, an improvement of 8.9%. The selected utterances can also provide valuable insights for suicide prevention research. Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.

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Multi-view Story Characterization from Movie Plot Synopses and Reviews

Cited by 3 publications

References 17 publications

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

Computational Storytelling and Emotions: A Survey

Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

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