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== 메타데이터 ==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q36846256 Q36846256]

2020년 12월 26일 (토) 03:59 판

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말뭉치

  1. The IMDB dataset We’ll be working with “IMDB dataset”, a set of 50,000 highly-polarized reviews from the Internet Movie Database.[1]
  2. Just like the MNIST dataset, the IMDB dataset comes packaged with Keras.[1]
  3. This is called sentiment analysis and we will do it with the famous IMDB review dataset.[2]
  4. The IMDB sentiment classification dataset consists of 50,000 movie reviews from IMDB users that are labeled as either positive (1) or negative (0).[2]
  5. Due to a recent change in the framework, Keras has some problems loading the IMDB dataset.[2]
  6. Continue downloading the IMDB dataset, which is, fortunately, already built into Keras.[2]
  7. We'll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database.[3]
  8. The Large Movie Review Dataset (often referred to as the IMDB dataset) contains 25,000 highly polar moving reviews (good or bad) for training and the same amount again for testing.[4]
  9. Next we will load the IMDB dataset.[4]
  10. We’ll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database.[5]
  11. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie.[6]
  12. The dataset also contains movie metadata such as date of release of the movie, run length, IMDb rating, movie rating (PG-13, R, etc), number of IMDb raters, and number of reviews per movie.[7]
  13. There is a very famous data set of movie reviews, is the IMDB dataset, and the guys at Keras have already preloaded it in their datasets.[8]
  14. The next step is to build the model and we've loaded a bunch of reviews from the IMDB dataset and our goal is to measure the sentiment of such reviews.[8]
  15. In this tutorial we will use the Large Movie Review Dataset (from Stanford University) which consists of 50,000 movie reviews (50% negative and 50% positive).[9]
  16. The demo uses the well-known IMDB movie review dataset.[10]
  17. recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files.[11]
  18. Here, we use the IMDB movie review dataset that consists of the 25000 train and 25000 test text data sample labelled by positive and negative.[12]
  19. This IMDB movie review dataset is published by Stanford AI Lab.[12]
  20. In this tutorial, you have discovered how to build a neural network model for text classification (sentiment analysis) of the IMDB movie review dataset using TensorFlow.[12]
  21. However, Line 2 shows that on the large IMDB dataset, the accuracy of the classifier is boosted with only bigrams as features.[13]
  22. Learning Word Vectors for Sentiment Analysis we make use of the ‘Large Movie Review Dataset’.[14]
  23. The IMDB dataset contains 50,000 movie reviews for natural language processing or Text analytics.[15]
  24. Instead of downloading the dataset we will be directly using the IMDB dataset provided by keras.[15]

소스

  1. ↑ 1.0 1.1 Classifying movie reviews: a binary classification example
  2. ↑ 2.0 2.1 2.2 2.3 How to Build a Neural Network With Keras Using the IMDB Dataset
  3. ↑ Text classification with TensorFlow Hub: Movie reviews
  4. ↑ 4.0 4.1 How to Predict Sentiment From Movie Reviews Using Deep Learning (Text Classification)
  5. ↑ Text Classification with IMDb Movie Reviews
  6. ↑ Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method
  7. ↑ IMDb Movie Reviews Dataset
  8. ↑ 8.0 8.1 Getting Started With Deep Learning: Improving Performance Course
  9. ↑ imdb
  10. ↑ Sentiment Analysis Using Keras -- Visual Studio Magazine
  11. ↑ Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models by Nehal Mohamed Ali, Marwa Mostafa Abd El Hamid, Aliaa Youssif :: SSRN
  12. ↑ 12.0 12.1 12.2 Machine Learning Tutorials
  13. ↑ Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics
  14. ↑ Sentiment Analysis via R, FeatureHashing and XGBoost
  15. ↑ 15.0 15.1 IMDB Review Sentiment Classification using RNN LSTM

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