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Main features\ninclude text processing, sentiment analysis, data\nvisualization, correlation analysis, and synthetic data\ngeneration. Text processing involves cleaning and preparing\ntweets by removing textual noise and irrelevant words.\nSentiment analysis extracts and accurately analyzes sentiments\nfrom tweet texts using advanced algorithms. Data visualization\ncreates various charts like word clouds and sentiment polarity\ngraphs for visual representation of data. Correlation analysis\nexamines and calculates the correlation between tweet\nsentiments and environmental variables such as weather\nconditions. Additionally, random tweets can be generated for\ntesting and evaluating the performance of analyses, empowering\nusers to effectively analyze and interpret 'Twitter' data for\nresearch and commercial purposes.", "License": "GPL-3", "Maintainer": "Leila Marvian Mashhad ", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-06 08:52:11 UTC", "User": "root" }, "Author": "Andriette Bekker [aut], Mohammad Arashi [aut], Leila Marvian\nMashhad [aut, cre], Priyanka Nagar [aut]", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev make libharfbuzz-dev libicu-dev libjpeg-dev\nlibpng-dev libtiff-dev libuv1-dev libwebp-dev libxml2-dev\nlibssl-dev libx11-dev zlib1g-dev", "Repository": "https://leilamarvian.r-universe.dev", "Date/Publication": "2024-08-20 02:49:34 UTC", "RemoteUrl": "https://github.com/cran/WeatherSentiment", "RemoteRef": "HEAD", "RemoteSha": "53bb11edd724cf4ab059b6a84fa388d6eddb931f", "MD5sum": "d7ce84feb51c749b37d60efdb9e9510b", "_user": 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