Name
Abstract | ||
|---|---|---|
Movie plot summaries are expected to reflect the genre of movies since many spectators read the plot summaries before deciding to watch a movie. In this study, we perform movie genre classification from plot summaries of movies using bidirectional LSTM (Bi-LSTM). We first divide each plot summary of a movie into sentences and assign the genre of corresponding movie to each sentence. Next, using the word representations of sentences, we train Bi-LSTM networks. We estimate the genres for each sentence separately. Since plot summaries generally contain multiple sentences, we use majority voting for the final decision by considering the posterior probabilities of genres assigned to sentences. Our results reflect that, training Bi-LSTM network after dividing the plot summaries into their sentences and fusing the predictions for individual sentences outperform training the network with the whole plot summaries with the limited amount of data. Moreover, employing Bi-LSTM performs better compared to basic Recurrent Neural Networks (RNNs) and Logistic Regression (LR) as a baseline. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICSC.2018.00043 | 2018 IEEE 12th International Conference on Semantic Computing (ICSC) |
Keywords | Field | DocType |
Movie genre classification,LSTM,Recurrent Neural Networks (RNNs) | Task analysis,Computer science,Recurrent neural network,Posterior probability,Feature extraction,Natural language processing,Artificial intelligence,Majority rule,Plot (narrative),Sentence,Semantics | Conference |
ISSN | ISBN | Citations |
2325-6516 | 978-1-5386-4409-6 | 2 |
PageRank | References | Authors |
0.46 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Mert Ertugrul | 1 | 16 | 6.91 |
| Pinar Karagoz | 2 | 154 | 28.34 |