论文标题
junlp@dravidian-codemix-fire2020：使用双向RNN和语言标签的代码混合推文的情感分类
JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags
论文作者
论文摘要
在过去的二十年中，情绪分析一直是一个积极的研究领域，最近，随着社交媒体的出现，对社交媒体文本的情感分析需求越来越不断增加。由于社交媒体文本不使用一种语言，并且在很大程度上是编码混合的，因此传统的情感分类模型无法产生可接受的结果。本文试图解决这个非常研究的问题，并使用双向LSTM和语言标记，以促进从社交媒体中提取的代码混合泰米尔语文本的情感标记。当对测试数据进行评估时，提出的算法分别获得了精度，回忆和F1分别为0.59、0.66和0.58。
Sentiment analysis has been an active area of research in the past two decades and recently, with the advent of social media, there has been an increasing demand for sentiment analysis on social media texts. Since the social media texts are not in one language and are largely code-mixed in nature, the traditional sentiment classification models fail to produce acceptable results. This paper tries to solve this very research problem and uses bi-directional LSTMs along with language tagging, to facilitate sentiment tagging of code-mixed Tamil texts that have been extracted from social media. The presented algorithm, when evaluated on the test data, garnered precision, recall, and F1 scores of 0.59, 0.66, and 0.58 respectively.
