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首页> 外文会议>IFIP WG 12.5 International workshops on artificial intelligence applications and innovations;Mining Humanistic Data Workshop;Workshop on 5G-Putting Intelligence to the Network Edge >Using Twitter to Predict Chart Position for Songs
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Using Twitter to Predict Chart Position for Songs

机译：使用Twitter预测歌曲的图表位置

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With the advent of social media, concepts such as forecasting and now casting became part of the public debate. Past successes include predicting election results, stock prices and forecasting events or behaviors. This work aims at using Twitter data, related to songs and artists that appeared on the top 10 of the Billboard Hot 100 charts, performing sentiment analysis on the collected tweets, to predict the charts in the future. Our goal was to investigate the relation between the number of mentions of a song and its artist, as well as the semantic orientation of the relevant posts and its performance on the subsequent chart. The problem was approached via regression analysis, which estimated the difference between the actual and predicted positions and moderated results. We also focused on forecasting chart ranges, namely the top 5, 10 and 20. Given the accuracy and F-score achieved compared to previous research, our findings are deemed satisfactory, especially in predicting the top 20.
机译：随着社交媒体的出现，诸如预测和现在投放这样的概念已成为公众辩论的一部分。过去的成功包括预测选举结果，股票价格以及预测事件或行为。这项工作旨在使用与出现在Billboard Hot 100排行榜前10名中的歌曲和艺术家有关的Twitter数据，对收集到的推文进行情感分析，以预测未来的排行榜。我们的目标是调查歌曲的提及次数与其艺术家之间的关系，以及相关帖子的语义取向及其在后续图表中的表现。通过回归分析解决了该问题，该分析估计了实际位置和预测位置与调整结果之间的差异。我们还专注于预测图表范围，即前5名，10名和20名。鉴于与以前的研究相比，该工具的准确性和F分数得到了令人满意的发现，尤其是在预测前20名时，我们的发现也令人满意。

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