텍스트 마이닝을 활용한 감정 비율 단어 그래프

Abstract

Unstructured data consisting of natural language such as SNS, papers, and questionnaire subjective question answers are generally compared or visualized using text mining. When analyzing text data by setting a random interval representing a time such as year, quarter, month, and day, it may be necessary to determine which interval has the most data, which sentiment words are used a lot for each interval, and how much text data in a particular interval is written negatively than positive. As a way to solve this problem, this study proposes an sentiment ratio word graph that can deliver the above three information at once. An sentiment ratio word graph is a graph created by dividing text data by a random interval standard representing time such as year, quarter, month, and day of the week and then assigning the sentiment score in the sentiment dictionary to the text data. When visualizing an sentiment ratio word graph, if you also use the pie coefficient to display words, you can further convey information about which words are most relevant to the sentiment word in a particular interval.