论文标题
Baksa在Semeval-2020任务中
BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
论文作者
论文摘要
对混合文本的情感分析在意见开采中具有多元化的应用程序，从标记用户评论到确定子人群的社会或政治情感。在本文中，我们介绍了卷积神经网（CNN）和基于自我注意力的LSTM的合奏结构，用于对混合推文的情感分析。尽管CNN组件有助于对正面和负面推文的分类，但基于自我注意力的LSTM有助于对中性推文的分类，因为它能够识别多种情感轴承单位之间的正确情感。我们在印度英语（Hinglish）和西班牙语 - 英语（Spanglish）数据集中获得了0.707（排名第五）和0.725（排名第13位）的F1分数。在用户名Ayushk和Harsh_6下进行了hinglish和Spanglish任务的提交。
Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population. In this paper, we present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets. While the CNN component helps in the classification of positive and negative tweets, the self-attention based LSTM, helps in the classification of neutral tweets, because of its ability to identify correct sentiment among multiple sentiment bearing units. We achieved F1 scores of 0.707 (ranked 5th) and 0.725 (ranked 13th) on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively. The submissions for Hinglish and Spanglish tasks were made under the usernames ayushk and harsh_6 respectively.
