A multimodal approach for multi-label movie genre classification

Mangolin, Rafael B.; Pereira, Rodolfo Miranda; Britto, Alceu S.; Silla, Carlos N.; Feltrim, Valéria Delisandra; Bertolini, Diego; Costa, Yandre M. G.

doi:10.1007/s11042-020-10086-2

Cited by 31 publications

(9 citation statements)

References 42 publications

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“…Documents may be classified according to multiple labels or classes simultaneously and independently, as indicated by multi-label classification. The multi-label classification has numerous realworld applications, such as categorizing businesses or assigning multiple genres to a film [31]. It can be used in customer service to determine multiple intentions for a customer email [32].…”

Section: Bert As a Deep Learning Methodsmentioning

confidence: 99%

Twitter-based classification for integrated source data of weather observations

Purwandari

Cenggoro²,

Sigalingging³

et al. 2023

IJ-AI

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<span lang="EN-US">Meteorology and weather forecasting are crucial for predicting future climate conditions. Forecasts can be helpful when they provide information that can assist people in making better decisions. People today use big data to analyze social media information accurately, including those who rely on the weather forecast. Recent years have seen the widespread use of machine learning and deep learning for managing messages on social media sites like Twitter. In this study, authors analyzed weather-related text in Indonesia based on the searches made on Twitter. A total of three machine learning algorithms were examined: support vector machine (SVM), multinomial logistic regression (MLR), and multinomial Naive Bayes (MNB), as well as the pretrained bidirectional encoder representations of transformers (BERT), which was fine-tuned over multiple layers to ensure effective classification. The accuracy of the BERT model, calculated using the F1-score of 99%, was higher than that of any other machine learning method. Those results have been incorporated into a web-based weather information system. The classification result was mapped using Esri Maps application programming interface (API) based on the geolocation of the data.</span>

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Section: Bert As a Deep Learning Methodsmentioning

confidence: 99%

Twitter-based classification for integrated source data of weather observations

Purwandari

Cenggoro²,

Sigalingging³

et al. 2023

IJ-AI

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“…As mentioned earlier, the growth of the rating system through reviews in recent years has been market-driven: when thinking about streaming services, it is evident how the need to provide suggestions that are increasingly in line with user needs has required a refinement of multi-label rating systems to identify the different genres in which to frame the products offered, such as in music [38], [39] or movie [40], [41] streaming services.…”

Section: Multiple Labels: Problem Transformation Methodsmentioning

confidence: 99%

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

et al. 2023

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The growth of the online review phenomenon, which has expanded from specialised trade magazines to end users via online platforms, has also increasingly involved the cultural heritage of countries, a source of tourism and growth driver of local economies. Unfortunately, this has been paralleled by the emergence and spread of the phenomenon of fake reviews, against which the scientific world has developed language models capable of distinguishing them from the truthful. The application of such models, often based on deep neural networks with transformer-type architectures, is however limited by the availability of local language data sets for specific domains, useful for both training and verification. The purpose of this article is twofold. Firstly, a new data set was created in the Italian language, generally considered low-resource, relating to the domain of cultural heritage in Italy, by collecting reviews available online, reorganising them in the form of a data set usable by the language models. Secondly, a baseline of results for the detection of misleading reviews was constructed by exploiting two widely used language models, namely BERT and ELECTRA. The performance achieved is interesting, around 95% accuracy and F1 score, using data set splits between training and testing of 80/20 and 90/10. In addition, SHAP was used as a tool to support the explicability of AI models: in this way, it was possible to show the usefulness of sentiment analysis as a support for the recognition of deceptiveness.INDEX TERMS Italian cultural heritage, data set, fake reviews, sentiment analysis, deceptive.

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“…Some studies, such as Kundalia, Patel and Shah (2020), applied film posters instead of text to identify the genre. In Mangolin, Pereira, Britto, Silla, Feltrim, Bertolini and Costa (2020) subtitles and introductory movies used to identify film genres (such as action, adventure, romance, etc.). In Wehrmann and Barros (2017), using a deep neural network, film images examined to identify the genre of the film.…”

Section: A Review Of Research Literaturementioning

confidence: 99%

“…); Chen et al (2017); Ibrahim et al (2019); Khattar et al (2019); Anwar et al (2013); Luhmann, Burghardt and Tiepmar (2021) 2-Predicting the film genre Lee (2017); Katsiouli et al (2007); Wehrmann and Barros (2017); Fourati et al (2014); Hong and Hwang (2015); Saumya et al (2018) and 3-Predicting the success rate (rating)Kundalia et al (2020);Mangolin et al (2020). However, no significant work has been done on age classification Xia…”

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confidence: 99%

A Movie recommender system based on topic modeling using machine learning methods

Kordabadi

Nazari

Mansoorizadeh

2022

Preprint

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In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user’s age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user’s age and recommend movies based on the user’s age with 93% accuracy, which is highly satisfactory.

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A multimodal approach for multi-label movie genre classification

Cited by 31 publications

References 42 publications

Twitter-based classification for integrated source data of weather observations

Twitter-based classification for integrated source data of weather observations

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

A Movie recommender system based on topic modeling using machine learning methods

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