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Persian sentiment lexicon expansion using unsupervised learning methods

Akhoundzade, R ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/ICCKE48569.2019.8964692
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Sentiment analysis, is a subfield of natural language processing that aims at opinion mining to analyze thoughts, orientation and, evaluation of users within some texts. The solution to this problem includes two main steps: extracting aspects and determining users' positive or negative sentiments with respect to the aspects. Two main challenges of sentiment analysis in the Persian language are lack of comprehensive tagged data sets and use of colloquial language in texts. In this paper we propose, a system to specify and extract sentiment words using unsupervised methods in the Persian language that also support colloquial words. Additionally, we also proposed and implemented a state-of-art technique to expand Persian sentiment lexicon. Our proposed method utilized neural network (Word2Vec model) with the help of rule-based methods. F1 measure for sentiment words extraction in our proposed method is 0.58. © 2019 IEEE
  6. Keywords:
  7. Aspect extraction ; Learning methods ; Rule-based methods ; Sentiment analysis ; Topic modeling ; Unsupervised methods ; Extraction ; Knowledge engineering ; Neural networks ; Text mining ; Unsupervised learning ; Precision ; Recall ; Rule-based method ; Unsupervised method ; Learning systems
  8. Source: 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019, 24 October 2019 through 25 October 2019 ; 2019 , Pages 461-465 ; 9781728150758 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/8964692