Mining Affective Context in Short Films for Emotion-Aware Recommendation

Orellana-Rodriguez, Claudia; Diaz-Aviles, Ernesto; Nejdl, Wolfgang

doi:10.1145/2700171.2791042

Cited by 27 publications

(22 citation statements)

References 25 publications

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“…For these sentiment categories, we utilize the NRC Word-Emotion Association Lexicon (EmoLex) [26,27], which is a widely used emotion word lexicon that has been used in many other works [3,29,33]. The EmoLex lexicon comprises 10,170 words that are associated with the emotions of anger, anticipation, disgust, fear, joy, sadness, surprise and trust, introduced in Plutchik's theory of emotions [31].…”

Section: Sentiment Analysismentioning

confidence: 99%

The Grass is Greener on the Other Side

Lim

Lee

Kendal

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research.

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Section: Sentiment Analysismentioning

confidence: 99%

The Grass is Greener on the Other Side

Lim

Lee

Kendal

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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“…Illustrated below are samples of a few recent works. Orellana-Rodriguez [10] [11] advocated that instead of detecting the affective polarity features (i.e., positive/negative) of a given short video in YouTube, they detect the paired eight basic human emotions advocated by Plutchik [12] [13] into four opposing pairs of basic moods: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. Orellana-Rodriguez [10] also leveraged the auto extraction of film metadata's moods context for making emotion-aware movie recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…Orellana-Rodriguez [10] [11] advocated that instead of detecting the affective polarity features (i.e., positive/negative) of a given short video in YouTube, they detect the paired eight basic human emotions advocated by Plutchik [12] [13] into four opposing pairs of basic moods: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. Orellana-Rodriguez [10] also leveraged the auto extraction of film metadata's moods context for making emotion-aware movie recommendations. Qian et al [14] proposed an EARS based on hybrid information fusion using user rating information as explicit data, user social network data as implicit information, and sentiment from user reviews as the source of emotional information.…”

Section: Related Workmentioning

confidence: 99%

Text-based Emotion Aware Recommender

Leung¹,

Griva²,

Kennedy³

2020

Computer Science &Amp; Information Technology

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We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.

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“…Chen and Chen [19,93] sentiment of item aspects using the bootstrapping method proposed in [107] to identify the aspects and a opinion lexicon to detect the aspect sentiments Colace et al [94,95] sentiment of reviews using an improvement of the approach presented in [108], where the LDA is applied Kothari and Patel [96] based on [93] based on [93] Orellana et al [ We emphasize that, in addition to the organization by type of contextual information and the use of opinion mining described in Tables 1-3, the graph analysis complements the visualization of the revised works by showing the main characteristics in common from a statistical point of view. The group with papers [90,92,98,99,103] has, as a main characteristic, the use of location and time as contextual information, while the group with papers [97,105] more directly explores emotion and affective information as a context in the recommendation system. The group with papers [19,[93][94][95][96] focuses on analyzing user's preference extracted from opinion mining and on the use of this information as context-dependent preferences.…”

Section: Reference Opinion Information How Opinion Information Is Extmentioning

confidence: 99%

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

et al. 2019

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Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.

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Mining Affective Context in Short Films for Emotion-Aware Recommendation

Cited by 27 publications

References 25 publications

The Grass is Greener on the Other Side

The Grass is Greener on the Other Side

Text-based Emotion Aware Recommender

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

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