An Annotated Bibliography on Screenwriting Manuals between 2000-2020 that aid in Character Development

<p>The New Zealand Film Commission (NZFC) is the government organisation that finances films. The NZFC agrees with many screenwriting authors that the most common problems with scripts relate to character development (Batty, 2011, p. 45; Frayne, 2019; Nelmes, 2010, p. 202; NZFC Scriptwriting, 2020). Therefore, a screenwriter needs resources to overcome character development problems in their scripts. However, there are too many screenwriting manuals in publication. Also, an inexperienced amateur screenwriter may not know which manuals would solve their particular character development problems. In this annotated bibliography, the screenwriter is the author responsible for developing the script. Furthermore, the script serves as a guideline to producing media content: feature film, short film, videogames, radio-play, television (Field, 2005, p. 19; Gallo, 2012a, Chapter 2; Nannicelli, 2013, pp. 11–18). On account of this, this annotated bibliography will focus mainly on screenwriting manuals dealing with feature film scripts. Feature film scripts are 90 minutes or longer in duration (Field, 2005, p. 22). Additionally, since a script encompasses story and character elements, this annotated bibliography will focus only on character development.</p>

An Annotated Bibliography on Screenwriting Manuals between 2000-2020 that aid in Character Development

<p>The New Zealand Film Commission (NZFC) is the government organisation that finances films. The NZFC agrees with many screenwriting authors that the most common problems with scripts relate to character development (Batty, 2011, p. 45; Frayne, 2019; Nelmes, 2010, p. 202; NZFC Scriptwriting, 2020). Therefore, a screenwriter needs resources to overcome character development problems in their scripts. However, there are too many screenwriting manuals in publication. Also, an inexperienced amateur screenwriter may not know which manuals would solve their particular character development problems. In this annotated bibliography, the screenwriter is the author responsible for developing the script. Furthermore, the script serves as a guideline to producing media content: feature film, short film, videogames, radio-play, television (Field, 2005, p. 19; Gallo, 2012a, Chapter 2; Nannicelli, 2013, pp. 11–18). On account of this, this annotated bibliography will focus mainly on screenwriting manuals dealing with feature film scripts. Feature film scripts are 90 minutes or longer in duration (Field, 2005, p. 22). Additionally, since a script encompasses story and character elements, this annotated bibliography will focus only on character development.</p>

Mood Classification of Balinese Songs with the K-Means Clustering Method Based on the Audio-Content Feature

Bali is a province that has a diversity of arts and can not shunt from songs that come from Bali. Music in Balinese songs has a unique character, both in the variations of the tone that builds up a song and the lyrics contained in a Balinese song. Research on the classification of mood with energy and valence features of a song is often done, especially on western songs. Every music that is thought out has emotional energy that radiates and powerfully connects with human psychology. This research wants to explore whether the features used to classify western songs can also classify Balinese songs, which are rich in the sound of musical instruments according to the tastes of the Balinese themselves. Classification of songs is essential, considering that music is related to specific emotions and moods in humans. In this study, the mood classification of Balinese songs is performed using the Spotify API feature, namely energy and valence. Classification using K-means clustering based on energy and valence features is compared with the song mood data from ten respondents and produces the highest accuracy of 32%.

Agricultural Transfer Labor Wage Platform Based on Image Content Feature Retrieval and Internet of Things

Quick and accurate information identification of agricultural transfer labor wage platform is an essential function of labor intelligent management in the new era. Based on the content feature retrieval, this study constructs an artificial intelligence identity information recognition system and links the system to the salary platform. Simultaneously, this study uses the feature recognition to extract database information and realize intelligent salary assessment. In addition, the deep learning features used in this study are based on the positional information of the sift features and are finally calculated by the activation map to obtain a global vector of an image. Finally, this study design testing experiment to verify the performance of the algorithm. Through the output of the feature picture, it can be seen that the research algorithm has certain effects and can be used as a follow-up system practice.

A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

Background Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. Objective We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. Methods We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. Results There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. Conclusions We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis (Preprint)

BACKGROUND Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. OBJECTIVE We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. METHODS We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. RESULTS There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. CONCLUSIONS We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation

Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects.