CS 224D Final Project: Neural Network Ensembles for Sentiment Classification
نویسنده
چکیده
We investigate the effect of ensembling on two simple models: LSTM and bidirectional LSTM. These models are used for fine-grained sentiment classification on the Stanford Sentiment Treebank dataset. We observe that ensembling improves the classification accuracy by about 3% over single models. Moreover, the more complex model, bidirectional LSTM, benefits more from ensembling.
منابع مشابه
CS 224D Final Project DeepRock
We create a canonical encoding for multi-instrument MIDI songs into natural language, then use deep NLP techniques such as character LSTM variants to compose rock music that surpasses the prior state of the art and is competitive with certain pieces of music composed by human rock bands. We further define a neural network architecture for learning multi-instrument music generation in concert, b...
متن کاملA High-Performance Model based on Ensembles for Twitter Sentiment Classification
Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a sentiment ta...
متن کاملCS 224D: Deep Learning for NLP
Keyphrases: Intrinsic and extrinsic evaluations. Effect of hyperparameters on analogy evaluation tasks. Correlation of human judgment with word vector distances. Dealing with ambiguity in word using contexts. Window classification. This set of notes extends our discussion of word vectors (interchangeably called word embeddings) by seeing how they can be evaluated intrinsically and extrinsically...
متن کاملCS229 Final Project Sentiment Analysis of Tweets: Baselines and Neural Network Models
The goal of sentiment analysis is to classify text samples according to their overall positivity or negativity. We refer to the positivity or negativity of a text sample as its polarity. In this project, we investigate three-class sentiment classification of Twitter data where the labels are “positive”, “negative”, and “neutral”. We explore a number of questions in relation to the sentiment ana...
متن کاملCS224N Final Project Sentiment Analysis of Tweets: Baselines and Neural Network Models
The goal of sentiment analysis is to classify text samples according to their overall positivity or negativity. We refer to the positivity or negativity of a text sample as its polarity. In this project, we investigate three-class sentiment classification of Twitter data where the labels are “positive”, “negative”, and “neutral”. We explore a number of questions in relation to the sentiment ana...
متن کامل