Wei, Miao
(2017)
Sentiment Analysis Using Deep
Learning: A Comparison Between
Chinese And English.
Masters thesis, National University of Ireland Maynooth.
Abstract
With the increasing popularity of opinion-rich resources, opinion mining and
sentiment analysis has received increasing attention. Sentiment analysis is one of
the most effective ways to find the opinion of authors. By mining what people think,
sentiment analysis can provide the basis for decision making. Most of the objects of
analysis are text data, such as Facebook status and movie reviews. Despite many
sentiment classification models having good performance on English corpora, they
are not good at Chinese or other languages. Traditional sentiment approaches
impose many restrictions on the raw data, and they don't have enough capacity to
deal with long-distance sequential dependencies.
So, we propose a model based on recurrent neural network model using a
context vector space model. Chinese information entropy is typically higher than
English, we therefore hypothesise that context vector space model can be used to
improve the accuracy of sentiment analysis. Our algorithm represents each complex
input by a dense vector trained to translate sequence data to another sequence, like
the translation of English and French. Then we build a recurrent neural network with
the Long-Short-Term Memory model to deal the long-distance dependencies in input
data, such as movie review. The results show that our approach has promise but still
has a lot of room for improvement.
| Item Type: |
Thesis
(Masters)
|
| Keywords: |
Sentiment Analysis; Deep Learning; Comparison;
Chinese; English; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
9903 |
| Depositing User: |
IR eTheses
|
| Date Deposited: |
11 Sep 2018 14:08 |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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