Twitter and other social media platforms are often used for sharing interest in products. The identification of purchase decision stages, such as in the AIDA model (Awareness, Interest, Desire, and Action), can enable more personalized e-commerce services and a finer-grained targeting of advertisements than predicting purchase intent only. In this paper, we propose and analyze neural models for identifying the purchase stage of single tweets in a user’s tweet sequence. In particular, we identify three challenges of purchase stage identification: imbalanced label distribution with a high number of non-purchase-stage instances, limited amount of training data, and domain adaptation with no or only little target domain data. Our experiments reveal that the imbalanced label distribution is the main challenge for our models. We address it with ranking loss and perform detailed investigations of the performance of our models on the different output classes. In order to improve the generalization of the models and augment the limited amount of training data, we examine the use of sentiment analysis as a complementary, secondary task in a multitask framework. For applying our models to tweets from another product domain, we consider two scenarios: for the first scenario without any labeled data in the target product domain, we show that learning domain-invariant representations with adversarial training is most promising, while for the second scenario with a small number of labeled target examples, fine-tuning the source model weights performs best. Finally, we conduct several analyses, including extracting attention weights and representative phrases for the different purchase stages. The results suggest that the model is learning features indicative of purchase stages and that the confusion errors are sensible.

中文翻译：

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从不平衡的推特数据中解决神经购买阶段识别的挑战

Twitter 和其他社交媒体平台通常用于分享对产品的兴趣。识别购买决策阶段，例如在 AIDA 模型（意识、兴趣、欲望和行动）中，可以实现更个性化的电子商务服务和更精细的广告定位，而不是仅预测购买意图。在本文中，我们提出并分析了用于识别用户推文序列中单个推文购买阶段的神经模型。特别是，我们确定了购买阶段识别的三个挑战：具有大量非购买阶段实例的不平衡标签分布、有限数量的训练数据以及没有或只有很少目标域数据的域适应。我们的实验表明，不平衡的标签分布是我们模型的主要挑战。我们通过排名损失来解决它，并对我们的模型在不同输出类别上的性能进行详细调查。为了提高模型的泛化能力并增加有限数量的训练数据，我们研究了将情感分析用作多任务框架中的补充性次要任务。为了将我们的模型应用于来自另一个产品领域的推文，我们考虑了两种情况：对于在目标产品领域中没有任何标记数据的第一种情况，我们表明通过对抗性训练学习域不变表示是最有希望的，而对于第二种情况对于少量标记的目标示例，微调源模型权重的效果最好。最后，我们进行了几项分析，包括提取不同购买阶段的注意力权重和代表性短语。结果表明，该模型正在学习指示购买阶段的特征，并且混淆错误是明智的。