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A Study on the Method for Automatically Constructing a Domain Specific Sentiment Lexicon Based Lexical Relation and Contextual Information
http://doi.org/10.5626/JOK.2020.47.10.926
A sentiment lexicon is a set of sentiment words, each of which has its sentiment polarity, and is used as a basic method for sentiment analysis. However, the meaning of some words can be different or even the original meaning can disappear across domains. As such, many sentiment words are likely to depend on a specific domain. For example, the verb phrase “slept well” usually has a negative meaning, while it has a positive meaning in movie domains. Thus, given a particular domain such as hotel, the sentiment lexicon should be constructed so that many of the domain-dependent words reflect the meaning of the domain. Using the domain-dependent sentiment lexicon will render more accurate results than using existing sentiment lexicons that do not consider domain-dependent words in the sentiment analysis. To build the domain-dependent sentiment lexicons, various studies have been presented, but there are many limitations including the human intervention and the use of local information rather than contextual information. In this paper, we propose a novel method of automatically constructing a domain-dependent sentiment lexicon based on the global and contextual information and an existing sentiment lexicon (i.e., KNU sentiment lexicon, Glove vector, Conjunction relation).
Automatic Generation of HTML Code Based on Web Page Sketch
Bada Kim, Sangmin Park, Taeyeon Won, Junyung Heo
http://doi.org/10.5626/JOK.2019.46.1.9
Various studies have been conducted to automatically encode GUI designs in web application development. In the past study, the focus was on object region detection using computer vision and object detection based on deep-learning. The past reported study had the limitations of incorrect detection or no detection of the object. In the present work, two technologies were applied collectively to reduce the limitations of conventional object detection. The computer vision is used for layout detection, and deep-learning is used for GUI object detection. Based on these technologies, detected layouts and GUI objects were converted into HTML code. Consequently, the accuracy and recall rate of GUI object detection were 91% and 86%, respectively, and it was possible to convert into HTML code.
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