35,692 research outputs found
Sentiment Analysis Using Deep Learning: A Comparison Between Chinese And English
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
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
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