Large Scale Sentiment Learning with Limited Labels
Large Scale Sentiment Learning with Limited Labels
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DOI：
10.1145/3097983.3098159
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发表时间：
2017-08
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影响因子：
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通讯作者：
Vasileios Iosifidis;Eirini Ntoutsi
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作者：
Vasileios Iosifidis;Eirini Ntoutsi
情绪分析是一项重要的任务，以便深入了解每天在社交媒体中产生的大量意见。虽然在情感分析方面有很多工作，但没有太多数据集可用于开发新方法和评估。据我们所知，最大的情感分析数据集是TSentiment [8]，这是一个160万条机器注释的推文数据集，涵盖了2009年约3个月的时间。然而，这个数据集太短，因此不足以研究异质的，快速演变的流。因此，我们对2015年的Twitter数据集（2.28亿条未转发的推文和2.75亿条有转发的推文）进行了注释，并将其公开用于研究。对于注释，我们利用了未标记数据的强大功能，以及使用半监督学习的标记数据，特别是自学习和协同训练。我们的主要贡献是提供TSentiment15数据集以及分析中的见解，其中包括数据的批处理和流处理。在前者中，所有标记和未标记的数据从一开始就可用于算法，而在后者中，它们是基于它们在流中的到达时间逐渐显示的。
Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.