论文标题
情感分析和讽刺印度大选推文
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets
论文作者
论文摘要
在当今的数字世界中，社交媒体使用率已提高到历史最高水平。大多数人群都使用社交媒体工具（例如Twitter，Facebook，YouTube等）与社区分享他们的想法和经验。分析普通公众的情感和观点对政府和商人都非常重要。这是许多媒体机构在选举期间进行各种民意调查的背后的原因。在本文中，我们旨在使用该持续时间的Twitter数据在2019年Lok Sabha选举期间分析印度人民的情感。我们已经使用转移学习技术构建了一个自动推文分析仪，以处理该问题的无监督性质。我们在机器学习模型中使用了线性支持向量分类器方法，此外，术语频率逆文档频率（TF-IDF）方法来处理推文的文本数据。此外，我们提高了该模型的能力，可以解决一些用户发布的讽刺推文，该域中尚未考虑该领域的研究人员。
Social Media usage has increased to an all-time high level in today's digital world. The majority of the population uses social media tools (like Twitter, Facebook, YouTube, etc.) to share their thoughts and experiences with the community. Analysing the sentiments and opinions of the common public is very important for both the government and the business people. This is the reason behind the activeness of many media agencies during the election time for performing various kinds of opinion polls. In this paper, we have worked towards analysing the sentiments of the people of India during the Lok Sabha election of 2019 using the Twitter data of that duration. We have built an automatic tweet analyser using the Transfer Learning technique to handle the unsupervised nature of this problem. We have used the Linear Support Vector Classifiers method in our Machine Learning model, also, the Term Frequency Inverse Document Frequency (TF-IDF) methodology for handling the textual data of tweets. Further, we have increased the capability of the model to address the sarcastic tweets posted by some of the users, which has not been yet considered by the researchers in this domain.
