| dc.description.abstract |
With the development of technology and the Internet, different types of social media such as social
networks and forums have allowed people to not only share information but also to express their
opinions and attitudes on products, services and other social issues. The Internet becomes a very
valuable and important source. People nowadays use it as a reference to make their decisions on
buying a product or using a service. Moreover, this kind of information also let the manufacturers
and service providers receive feedback about limitations of their products. Therefore, that
information may help them to improve their products in order to better meet the customer needs.
Public opinions can also help authorities know the attitudes and opinions of their residents on
social events so that they can make appropriate adjustments. The large amount of data from the
Internet increases the demand to develop automatic system that can identify and classify
sentiments in an opinion.
Since early 2000s, opinion mining and sentiment analysis have become a new and active research
topic in Natural language processing and Data mining. However, research in sentiment analysis
for Vietnamese is relatively new and there is no systematic comparison between the performance2
of Vietnamese sentiment analysis systems. In this thesis, I will present some effective models for
classifying Vietnamese sentences based on Support Vector Machine (SVM), an advanced
Supervised Learning technique. The scope is to evaluate the ability of classifying Vietnamese
reviews into one of three categories: “positive”, “negative”, or “neutral”. Results from different
feature sets and classifiers are reported in term of accuracy, which shows the effectiveness of each
method |
en_US |
