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Andrea, Ajello ; Diego, Silva ; Francisco, Vazquez-Grande ; Andrea, Ajello ; Travis, Adams. (2023) More than Words: Twitter Chatter and Financial Market Sentiment.
In: Finance and Economics Discussion Series. RePEc:fip:fedgfe:2023-34.
Full description at Econpapers
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However, certain challenges arise when working with Twitter data. The high volume of tweets available on different topics raises the challenge to obtain a pertinent set of tweets to the research question. Researchers use Google-like keyword searches to filter tweets, and, without careful consideration, can lead to overly broad or overly narrow data samples. In addition to this, most Twitter user accounts are not verified, allowing for robot-generated tweets inside the data. Furthermore, due to its relatively recent founding, in 2006, Twitter data analysis is only possible for most recent years. The number of tweets remains low until the user-base reached a critical mass. Given the challenges mentioned above, we designed a carefully curated query to download a feasible set of finance-related tweets using multiple Natural Language Processing tools, such as heuristic operators (’AND’,’OR’) and semantic similarity between words. A.2 How to Construct the Query.
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