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funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter.

, , , , and . SemEval@ACL, page 852-856. Association for Computational Linguistics, (2017)

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Information Mining: Integrating Data Mining and Text Mining for Business Intelligence., and . AMCIS, page 182. Association for Information Systems, (2006)Sexual Harassment Story Classification and Key Information Identification., , , , and . CIKM, page 2385-2388. ACM, (2019)TweetSenti: Target-dependent Tweet Sentiment Analysis., , and . WWW, page 3569-3573. ACM, (2019)funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts., , , , and . SemEval@ACL, page 741-746. Association for Computational Linguistics, (2017)Table classification using both structure and content information: A case study of financial documents., , and . IEEE BigData, page 1778-1783. IEEE Computer Society, (2016)Reuters tracer: Toward automated news production using large scale social media data., , , , , and . IEEE BigData, page 1483-1493. IEEE Computer Society, (2017)KIP: A Keyphrase Identification Program with Learning Functions., , , and . ITCC (2), page 450-454. IEEE Computer Society, (2004)isbn: 0-7695-2108-8.Court Opinion Generation from Case Fact Description with Legal Basis., and . AAAI, page 14840-14848. AAAI Press, (2021)Twitter Event Summarization by Exploiting Semantic Terms and Graph Network., and . AAAI, page 15347-15354. AAAI Press, (2021)Data Sets: Word Embeddings Learned from Tweets and General Data., , , and . CoRR, (2017)