Title page for etd-0803118-124937


[Back to Results | New Search]

URN etd-0803118-124937
Author Bing-Hung Lin
Author's Email Address No Public.
Statistics This thesis had been viewed 5666 times. Download 37 times.
Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title An Integrated Framework for Identifying Entities Topics and Sentiment from Text
Date of Defense 2018-07-23
Page Count 46
Keyword
  • Entity Extraction
  • Text Mining
  • Sentiment Analysis
  • Topic Identification
  • Chinese Natural Language Processing
  • Topic modeling
  • Aspect and Sentiment Unification Model
  • Abstract Due to the growth of news articles on the internet, topic and sentiment analysis have been widely used for text mining. However, it's difficult to identify topics and sentiments simultaneously in entity-level, especially in Chinese articles. To solve this problem, our approach provides an integrated framework for identifying entities, topics, and sentiments from texts. We use our algorithm to split documents into sentences with entities and implement ASUM to identify topics and sentiments. In the end, we apply word2vec model, Gaussian similarity kernel, and complete-linkage agglomerative algorithm to generate results. To evaluate our method, we collect data from the news website of “Apple Daily”, and select politics section from 2013 to 2017. Comparing with other system in different level, the experimental results show that our framework in entity-level is effective in topics and sentiments identification.
    Advisory Committee
  • Chih-Ping Wei - chair
  • Wen-Chun Ni - co-chair
  • San-Yih Hwang - advisor
  • Files
  • etd-0803118-124937.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2018-09-03

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys