在Twitter选举分类中使用词嵌入
Using Word Embeddings in Twitter Election Classification
摘要：
词嵌入和卷积神经网络（CNN）在Twitter的各种分类任务中引起了广泛的关注，例如情感分类。然而，用于训练和生成词嵌入的配置对分类性能的影响在现有文献中尚未研究。在本文中，使用Twitter的选举分类任务，旨在检测选举相关的推文，我们调查的背景数据集用于训练的嵌入模型，上下文窗口大小和维数的词嵌入的分类性能的影响。通过比较两个词嵌入模型的分类结果，这两个模型使用不同的背景语料（例如维基百科文章和Twitter微帖子）进行训练，我们表明背景数据类型应该与Twitter分类数据集保持一致，以实现更好的性能。此外，通过评估使用各种上下文窗口大小和维度训练的词嵌入模型的结果，我们发现大的上下文窗口和维度大小更有利于提高性能。我们的实验结果还表明，使用词嵌入和CNN导致统计上显着的改善，在各种基线，如随机，支持向量机与TF-IDF和支持向量机与词嵌入。
英文摘要：
Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to train and generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance. By comparing the classification results of two word embedding models, which are trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data type should align with the Twitter classification dataset to achieve a better performance. Moreover, by evaluating the results of word embeddings models trained using various context window sizes and dimensionalities, we found that large context window and dimension sizes are preferable to improve the performance. Our experimental results also show that using word embeddings and CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
