Movie Genre Classification from Plot Summaries Using Bidirectional LSTM

Ertugrul, Ali Mert; Karagöz, Pınar

doi:10.1109/icsc.2018.00043

Cited by 38 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Despite these significant caveats, we have demonstrated that certain genres can be reliably recognized from their text‐based topic distribution, echoing work on both film and literature genre prediction (Ertugrul & Karagoz, 2018; Underwood, 2016b). This works best for those genres with the more characteristic motifs, though even more diffuse genres (e.g., suspense, action/adventure) can still be quite accurately classified.…”

Section: Discussionsupporting

confidence: 65%

“…With movie data, the most commonly addressed tasks have been the prediction of genre from content-based features and the use of genre in the development of user recommendation algorithms. Ertugrul and Karagoz (2018) for instance, used deep learning to model short plot summaries labeled by genre, with 1,600 examples of each genre. After training, they obtained F-scores for genre prediction-with comparable precision and recall-of between 61 and 77% depending on the genre predicted (horror scoring highest, thriller lowest).…”

Section: Machine Learning and Genrementioning

confidence: 99%

See 1 more Smart Citation

Genre analysis of movies using a topic model of plot summaries

Matthews

Glitre

2021

Asso for Info Science & Tech

View full text Add to dashboard Cite

Genre plays an important role in the description, navigation, and discovery of movies, but it is rarely studied at large scale using quantitative methods. This allows an analysis of how genre labels are applied, how genres are composed and how these ingredients change, and how genres compare. We apply unsupervised topic modeling to a large collection of textual movie summaries and then use the model's topic proportions to investigate key questions in genre, including recognizability, mapping, canonicity, and change over time. We find that many genres can be quite easily predicted by their lexical signatures and this defines their position on the genre landscape. We find significant genre composition changes between periods for westerns, science fiction and road movies, reflecting changes in production and consumption values. We show that in terms of canonicity, canonical examples are often at the high end of the topic distribution profile for the genre rather than central as might be predicted by categorization theory.

show abstract

Section: Discussionsupporting

confidence: 65%

Section: Machine Learning and Genrementioning

confidence: 99%

Genre analysis of movies using a topic model of plot summaries

Matthews

Glitre

2021

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Genre classification using movie synopses and synopses is being actively carried out with the advancement of natural language processing research. 17 Ertugrul and Karagoz 18 employed bidirectional long-and short-term memory to classify movies into genres based on story summaries. The number of genres was limited to four and only one label classification was utilized.…”

Section: Non-poster-based Movie Genre Classificationmentioning

confidence: 99%

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Korai

Bouk

Sindhi

2021

Yanbu Journal of Engineering and Science

View full text Add to dashboard Cite

A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.

show abstract

“…Bidirectional Long Short Term Memory is a neural network of Long Short Term Memory (LSTM) which consists of two layers of LSTM neural networks, namely the advanced LSTM layer to model the previous context and the backward LSTM layer to model each subsequent context [12]. Bidirectional LSTM is by connecting two hidden layers from opposite directions to the same output.…”

Section: Bidirectional Lstmmentioning

confidence: 99%

Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Isnain¹,

Sihabuddin²,

Suyanto³

2020

Indonesian J. Comput. Cybern. Syst.

View full text Add to dashboard Cite

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%.

show abstract

Movie Genre Classification from Plot Summaries Using Bidirectional LSTM

Cited by 38 publications

References 10 publications

Genre analysis of movies using a topic model of plot summaries

Genre analysis of movies using a topic model of plot summaries

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Contact Info

Product

Resources

About