DeepAI
Log In Sign Up

Hybrid Improved Document-level Embedding (HIDE)

06/01/2020
by   Satanik Mitra, et al.
0

In recent times, word embeddings are taking a significant role in sentiment analysis. As the generation of word embeddings needs huge corpora, many applications use pretrained embeddings. In spite of the success, word embeddings suffers from certain drawbacks such as it does not capture sentiment information of a word, contextual information in terms of parts of speech tags and domain-specific information. In this work we propose HIDE a Hybrid Improved Document level Embedding which incorporates domain information, parts of speech information and sentiment information into existing word embeddings such as GloVe and Word2Vec. It combine improved word embeddings into document level embeddings. Further, Latent Semantic Analysis (LSA) has been used to represent documents as a vectors. HIDE is generated, combining LSA and document level embeddings, which is computed from improved word embeddings. We test HIDE with six different datasets and shown considerable improvement over the accuracy of existing pretrained word vectors such as GloVe and Word2Vec. We further compare our work with two existing document level sentiment analysis approaches. HIDE performs better than existing systems.

READ FULL TEXT
08/14/2017

Sentiment Analysis by Joint Learning of Word Embeddings and Classifier

Word embeddings are representations of individual words of a text docume...
01/18/2018

Contextual and Position-Aware Factorization Machines for Sentiment Classification

While existing machine learning models have achieved great success for s...
07/19/2016

An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation

Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word...
07/29/2015

Document Embedding with Paragraph Vectors

Paragraph Vectors has been recently proposed as an unsupervised method f...
07/08/2017

Efficient Vector Representation for Documents through Corruption

We present an efficient document representation learning framework, Docu...
01/05/2020

Generating Word and Document Embeddings for Sentiment Analysis

Sentiments of words differ from one corpus to another. Inducing general ...