Kaggle Challenge: Bag of words meets bags of popcorn
This tutorial will help you get started with Word2Vec for natural language processing. It has two goals:
Basic Natural Language Processing: Part 1 of this tutorial is intended for beginners and covers basic natural language processing techniques, which are needed for later parts of the tutorial.
Deep Learning for Text Understanding: In Parts 2 and 3, we delve into how to train a model using Word2Vec and how to use the resulting word vectors for sentiment analysis.
Since deep learning is a rapidly evolving field, large amounts of the work has not yet been published, or exists only as academic papers. Part 3 of the tutorial is more exploratory than prescriptive -- we experiment with several ways of using Word2Vec rather than giving you a recipe for using the output.
To achieve these goals, we rely on an IMDB sentiment analysis data set, which has 100,000 multi-paragraph movie reviews, both positive and negative.
簡單來說就是使用 Word2Vec 對 IMDB 的電影評論進行情感分析，透過模型判斷該評論為正面或負面。
雖然網頁上是要求使用 Word2Vec，但我是使用它的延伸應用 Doc2Vec models.doc2vec – Doc2vec paragraph embeddings
訓練資料和測試資料各 25000筆，另包含沒有標註情感分數的額外訓練資料集 50000筆
| SVC | Decision Tree | Random Forest | Logistic Regression | KNN | |
|---|---|---|---|---|---|
| Accuracy | 0.837 | 0.639 | 0.808 | 0.833 | 0.709 |
| MSE | 0.159 | 0.0 | 0.0 | 0.163 | 0.202 |
| MAE | 0.159 | 0.0 | 0.0 | 0.163 | 0.202 |
