论文标题
nits-hishlish-sentimix在Semeval-2020任务9：使用合奏模型的代码混合社交媒体文本的情感分析
NITS-Hinglish-SentiMix at SemEval-2020 Task 9: Sentiment Analysis For Code-Mixed Social Media Text Using an Ensemble Model
论文作者
论文摘要
情感分析是解读句子情感并将其归类为正面，负面或中立的过程。近来，印度的活跃社交媒体用户数量大量涌入，这导致了很多非结构化的文本数据。由于印度人口通常都流利了印地语和英语，因此他们最终会产生混合的Hinglish社交媒体文本，即印地语语言的表达方式，用罗马脚本和其他英语单词旁边写成。充分理解这些文本中的概念的能力确实是必要的。我们的团队RNS2020参加了SEMEVAL2020的任务9，旨在设计一个系统以进行代码混合社交媒体文本的情感分析。这项工作提出了一个名为Nits-Hishlish-Sentimix的系统，以弥补此类代码混合的Hinglish文本的情感分析。所提出的框架在测试数据上记录了0.617的F评分。
Sentiment Analysis is the process of deciphering what a sentence emotes and classifying them as either positive, negative, or neutral. In recent times, India has seen a huge influx in the number of active social media users and this has led to a plethora of unstructured text data. Since the Indian population is generally fluent in both Hindi and English, they end up generating code-mixed Hinglish social media text i.e. the expressions of Hindi language, written in the Roman script alongside other English words. The ability to adequately comprehend the notions in these texts is truly necessary. Our team, rns2020 participated in Task 9 at SemEval2020 intending to design a system to carry out the sentiment analysis of code-mixed social media text. This work proposes a system named NITS-Hinglish-SentiMix to viably complete the sentiment analysis of such code-mixed Hinglish text. The proposed framework has recorded an F-Score of 0.617 on the test data.
