Exploring Textual Features for Multi-label Classification of Portuguese Film Synopses

Portolese, Giuseppe; Domingues, Marcos Aurélio; Feltrim, Valéria Delisandra

doi:10.1007/978-3-030-30244-3_55

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of natural language processing research, genre classification using plots or synopses of movies is being actively conducted [4], [17]- [20]. Ertugrul and Karagoz [21] performed genre classification based on movie plot summaries using bidirectional long short-term memory.…”

Section: B Non-poster-based Movie Genre Classificationmentioning

confidence: 99%

Poster-Based Multiple Movie Genre Classification Using Inter-Channel Features

Jang

Kim

2020

IEEE Access

View full text Add to dashboard Cite

As the scale of the film industry grows, the demand for well-established movie databases is also growing. The genre of a movie supplies information on its overall content and has multiple values. Therefore, it should be well classified utilizing the characteristics of movies, without omissions in the database. In this study, we extract the optimal information and characteristics from movie posters to aid the classification of movies into genres and propose the use of a Gram layer in a convolutional neural network (CNN). The Gram layer first extracts style features by applying the Gram matrix to produce a feature map of a poster image. Using this as a style weight, the existing feature map is merged with style information to perform the genre classification task. The proposed Gram layer performed multi-genre classification tasks with higher efficiency than a residual neural network (ResNet), which is the current CNN model used for such tasks. We compared the activation map with the Squeeze-and-Excitation network, which gives weight to the image, and we confirmed that the introduction of the Gram layer actually focuses on the style of the movie poster. To classify the movie genres, we reconstructed the poster dataset into 12 multi-genres that emphasized the characteristics of each poster.

show abstract

Section: B Non-poster-based Movie Genre Classificationmentioning

confidence: 99%

Poster-Based Multiple Movie Genre Classification Using Inter-Channel Features

Jang

Kim

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…54.8% of F-Score) was obtained using an MLP classifier fed with TF-IDF features based on 3-grams with dimensionality equal to 1,000. In [16], the authors extended their dataset to 13,394 movies (still with Portuguese synopses only) classified in 18 genres, and the groups of textual features. They also experimented with an oversampled version of the dataset.…”

Section: Related Workmentioning

confidence: 99%

“…The creation of this dataset was inspired by the one described by [16], which the authors also used a subset of TMDb, but composed only of Portuguese synopses from 13,394 movies. We used that subset of titles as a starting point, however, we focused on retrieving the text data sources (i.e.…”

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

A multimodal approach for multi-label movie genre classification

Mangolin¹,

Pereira²,

Britto³

et al. 2020

Preprint

View full text Add to dashboard Cite

Movie genre classification is a challenging task that has increasingly attracted the attention of researchers. The number of movie consumers interested in taking advantage of automatic movie genre classification is growing rapidly thanks to the popularization of media streaming service providers. In this paper, we addressed the multi-label classification of the movie genres in a multimodal way. For this purpose, we created a dataset composed of trailer video clips, subtitles, synopses, and movie posters taken from 152,622 movie titles from The Movie Database (TMDb) 1 . The dataset was carefully curated and organized, and it was also made available 2 as a contribution of this work. Each movie of the dataset was labeled according to a set of eighteen genre labels. We extracted features from these data using different kinds of descriptors, namely Mel Frequency Cepstral Coefficients (MFCCs), Statistical Spectrum Descriptor (SSD), Local Binary Pattern (LBP) with spectrograms, Long-Short Term Memory (LSTM), and Convolutional Neural Networks (CNN). The descriptors were evaluated using different classifiers, such as BinaryRelevance and ML-kNN. We have also investigated the performance of the combination of different classifiers/features using a late fusion strategy, which obtained encouraging results. Based on the F-Score metric, our best result, 0.628, was obtained by the fusion of a classifier created using LSTM on the synopses, and a classifier created using CNN on movie trailer frames. When considering the AUC-PR metric, the best result, 0.673, was also achieved by combining those representations, but in addition, a classifier based on LSTM created from the subtitles was used. These results corroborate the existence of complementarity among classifiers based on different sources of information in this field of application. As far as we know, this is the most comprehensive study developed in terms of the diversity of multimedia sources of information to perform movie genre classification.

show abstract