{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/constructing-financial-sentimental-factors-in","title":"Constructing Financial Sentimental Factors in Chinese Market Using Natural Language Processing","arxiv_id":"1809.08390","date":"2018-09-22","proceeding":null,"authors":["Junfeng Jiang","Jiahao Li"],"abstract":"In this paper, we design an integrated algorithm to evaluate the sentiment of\nChinese market. Firstly, with the help of the web browser automation, we crawl\na lot of news and comments from several influential financial websites\nautomatically. Secondly, we use techniques of Natural Language Processing(NLP)\nunder Chinese context, including tokenization, Word2vec word embedding and\nsemantic database WordNet, to compute Senti-scores of these news and comments,\nand then construct the sentimental factor. Here, we build a finance-specific\nsentimental lexicon so that the sentimental factor can reflect the sentiment of\nfinancial market but not the general sentiments as happiness, sadness, etc.\nThirdly, we also implement an adjustment of the standard sentimental factor.\nOur experimental performance shows that there is a significant correlation\nbetween our standard sentimental factor and the Chinese market, and the\nadjusted factor is even more informative, having a stronger correlation with\nthe Chinese market. Therefore, our sentimental factors can be important\nreferences when making investment decisions. Especially during the Chinese\nmarket crash in 2015, the Pearson correlation coefficient of adjusted\nsentimental factor with SSE is 0.5844, which suggests that our model can\nprovide a solid guidance, especially in the special period when the market is\ninfluenced greatly by public sentiment.","url_abs":"http://arxiv.org/abs/1809.08390v1","url_pdf":"http://arxiv.org/pdf/1809.08390v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"constructing-financial-sentimental-factors-in","repo_url":"https://github.com/Coldog2333/Financial-NLP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"constructing-financial-sentimental-factors-in","repo_url":"https://github.com/loicdiridollou/230t2-group3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"sse","method_name":"SSE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}