Hanhoon Kang

Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews

The existing senti-lexicon does not sufficiently accommodate the sentiment word that is used in the restaurant review. Therefore, this thesis proposes a new senti-lexicon for the sentiment analysis of restaurant reviews. When classifying a review document as a positive sentiment and as a negative sentiment using the supervised learning algorithm, there is a tendency for the positive classification accuracy to appear up to approximately 10% higher than the negative classification accuracy. This creates a problem of decreasing the average accuracy when the accuracies of the two classes are expressed as an average value. In order to mitigate such problem, an improved Naive Bayes algorithm is proposed. The result of the experiment showed that when this algorithm was used and a unigrams+bigrams was used as the feature, the gap between the positive accuracy and the negative accuracy was narrowed to 3.6% compared to when the original Naive Bayes was used, and that the 28.5% gap was able to be narrowed compared to when SVM was used. Additionally, the use of this algorithm based on the senti-lexicon showed an accuracy that improved by a maximum of 10.2% in recall and a maximum of 26.2% in precision compared to when SVM was used, and by a maximum of 5.6% in recall and a maximum of 1.9% in precision compared to when Naive Bayes was used.

Modeling Web Crawler Wrappers to Collect User Reviews on Shopping Mall with Various Hierarchical Tree Structure

Along with the increased number of internet shopping mall users, high quantities of reviews on products are often found in many shopping malls. In order to extract effective information from those reviews, many studies on opinion mining have been actively performed. Due to the various type of structure of shopping malls, it is difficult to apply a single web crawler on all the shopping malls, so proper web crawler models need to be implemented for each shopping mall. The core technique of constructing the appropriate web crawler is the Wrapper, and in this study, wrapper models for product reviewing web crawlers are suggested, designed, and implemented for four large shopping malls.

Accessing Positive and Negative Online Opinions

Nowadays, an increasing number of people review the comments on each item before they will purchase the commodities and services offered by online shopping malls, Internet blogs, or cafes. However, it is somewhat challenging to routinely read trough all of the comments. The purpose of this study is to introduce some methods to classify the positive or negative review pertaining to the blog comments on a movie written in Korean. For this purpose, a variety of algorithms was used to classify the reviews and allow feature-selection by applying the traditional machine learning method for classifying literature.

Design and Implementation of Web Crawler Wrappers to Collect User Reviews on Shopping Mall with Various Hierarchical Tree Structure

In this study, the wrapper database description language and model is suggested to collect product reviews from Korean shopping malls with multi-layer structures and are built in a variety of web languages. Above all, the wrapper based web crawlers have the website structure information to bring the exact desired data. The previously suggested wrapper based web crawler can collect HTML documents and the hierarchical structure of the target documents were only 2-3 layers. However, the Korean shopping malls in the study consist of not only HTML documents but also of various web language (JavaScript, Flash, and AJAX), and have a 5-layer hierarchical structure. A web crawler should have information about the review pages in order to visit the pages without visiting any non-review pages. The proposed wrapper contains the location information of review pages. We also propose a language grammar used in describing the location information.