https://scholars.lib.ntu.edu.tw/handle/123456789/488350
Title: | Fine-Grained Analysis of Financial Tweets. | Authors: | Chen, Chung-Chi Huang, Hen-Hsen HSIN-HSI CHEN |
Keywords: | Financial tweet; Opinion mining; Sentiment analysis | Issue Date: | 2018 | Start page/Pages: | 1943-1949 | Source: | Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018 | Abstract: | This paper decribes our experimental methods and results in FiQA 2018 Task 1. There are two subtasks: (1) to predict continuous sentiment score between -1 to 1, and (2) to determine which aspect(s) are related to the content of financial tweets. First, we propose a preprocessing procedure for decomposing financial tweets. Second, we collect over 334K labeled financial tweets to enlarge the scale of the experiments. Third, the sentiment prediction task is separated into two steps in this paper, i.e., (1) bullish/bearish and (2) sentiment degree. We compare the results of the CNN, CRNN and Bi-LSTM models. Besides, we further combine the results of the best models in both steps as the model of subtask 1. Finally, we make an investigation of aspects in depth, and propose some clues for dealing with the 14 aspects. © 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063792774&doi=10.1145%2f3184558.3191824&partnerID=40&md5=a96579b32f9779bd4c563da4eacfa556 | DOI: | 10.1145/3184558.3191824 | SDG/Keyword: | Long short-term memory; Sentiment analysis; World Wide Web; Best model; Experimental methods; Financial tweet; Fine-grained analysis; Prediction tasks; Sentiment analysis; Sentiment scores; Subtask; Finance |
Appears in Collections: | 資訊工程學系 |
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