# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 2 Related Work
The GMU neural network architecture incorporates an internal unit that is intended to identify a transitional representation based on various data types. Furthermore, a dataset known as Multimodal-IMDB (MM-IMDB) was curated to cater to multimodel systems, encompassing a total of 23 distinct genres. The feature extraction and classification stages for text data involved the utilization of n-gram, word2 vec, and RNN models. On the other hand, for image data, a pretrained VGG-Net, a redesigned 5-layer CNN network, and multilayer perceptron (MLP) were employed. The word2 vec and MLP algorithms yielded the highest level of success in text classification, achieving an F-score of 0.59%. The results of the image classification indicated that the pretrained models outperformed the models developed from scratch, with an F-score of 0.43%. The multimodal GMU network demonstrated the highest level of performance, as evidenced by its F-score of 0.63%. [PERSON] [18], in his study, performed genre classification by using movie plot summaries and machine-learning methods. The study utilized the bag of words and word2 vec techniques to extract features, and subsequently employed the Naive Bayes, XGBoost, and Gated Recurrent Unit (GRU) classifiers for classification purposes. The models were tested using the IMDB dataset, which comprises 20 distinct genres. The findings of the experiments indicated that the GRU neural networks exhibited the highest level of success, as evidenced by their Jaccard index score of 50.0%, F-score of 0.56, and hit rate of 80.5%. In their study, [PERSON] et al. [19] employed the bidirectional long short-term memory (Bi-LSTM) technique at the word-level to predict movie genres based on their summaries. The MoviLens dataset was utilized in the study, and four distinct genres were established for the purpose of evaluating the model. The findings demonstrated that the Bi-LSTM network outperformed both recurrent neural networks (RNNs) and logistic regression (LR) in terms of performance. The experimental findings indicated that the Bi-LSTM model exhibited the most favorable outcome, attaining a macro precision of 67.75%. [PERSON] et al. [20] introduced a multimodal approach for movie genre detection from movie trailers and sounds, as well as predicting interestingness by utilizing four different genres. The evaluation of multimodal content is conducted at an intermediate level of representation, whereby each episode is characterized by a distribution across various genres. The process of extracting features from videos and audio was carried out through the utilization of pretrained networks, namely ResNet-152 and Soundner, for the respective modalities. The audio and visual representations that ensued were employed in conjunction with another model to assess the level of interest. The Predicted Media Interest Estimation Task (PMIT) dataset was utilized to assess the efficacy of the proposed model. The hybrid model demonstrated a superior performance, exhibiting precision and recall metric values of 90% and 87%, respectively. The incorporation of a hybrid model for mid-range representations resulted in a 3% enhancement in the accuracy of genre prediction. [PERSON] et al. [21] proposed multiple methods based on deep learning for predicting film genre and success rate from plot summaries. A Multi-Language Movie Review (MLMRD) dataset was constructed, containing nine distinct genres, success ratings, and summaries of movies in multiple languages, ranging from Hindi to Japanese. For the study, CNN-based models with character embedding, LSTM-based models with word embedding, and hybrid models with word, character, and sentence embedding were constructed. Classification performances were compared among themselves and with SVM and random forest, two traditional methods. As a result of the study, it was determined that word embedding contributed more to classification performance than other embedding models. Deep models were discovered to be more effective than conventional ones.