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Data Sets: Word Embeddings Learned from Tweets and General Data., , , и . CoRR, (2017)AIR-JPMC@SMM4H'22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling., , , , , , и . SMM4H@COLING, стр. 160-162. Association for Computational Linguistics, (2022)AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models., , , и . SMM4H@COLING, стр. 135-137. Association for Computational Linguistics, (2022)Large Scale Financial Time Series Forecasting with Multi-faceted Model., , , , , и . ICAIF, стр. 472-480. ACM, (2023)Towards Earnings Call and Stock Price Movement., , , и . CoRR, (2020)Harnessing global expertise: A comparative study of expertise profiling methods for online communities., , , , и . Inf. Syst. Frontiers, 16 (4): 715-727 (2014)funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter., , , , и . SemEval@ACL, стр. 852-856. Association for Computational Linguistics, (2017)TipMaster: A Knowledge Base of Authoritative Local News Sources on Social Media., , , , и . AAAI, стр. 7825-7830. AAAI Press, (2018)Using paraphrases to improve tweet classification: Comparing WordNet and word embedding approaches., , , , , и . IEEE BigData, стр. 4014-4016. IEEE Computer Society, (2016)Tweet Topic Classification Using Distributed Language Representations., , , , и . WI, стр. 81-88. IEEE Computer Society, (2016)