论文标题
从Twitter推断政治偏好
Inferring Political Preferences from Twitter
论文作者
论文摘要
情感分析是对用户对实体或该实体某些方面的意见和情感自动分析的任务。社交媒体的政治情绪分析有助于政治战略家仔细检查政党或候选人的表现，并在实际选举之前即兴创作弱点。在选举期间，社交网络充斥着博客，聊天，辩论和关于政党和政客前景的讨论。生成的数据量很大，可以使用最新技术研究，分析和绘制推论。 Twitter是最受欢迎的社交媒体平台之一，使我们能够执行特定领域的数据准备。在这项工作中，我们选择通过使用经典的机器学习将其建模为文本分类问题来确定推文中存在的政治观点的倾向。与2020年德里选举有关的推文被提取并用于任务。在几种算法中，我们观察到支持向量机描绘了最佳性能。
Sentiment analysis is the task of automatic analysis of opinions and emotions of users towards an entity or some aspect of that entity. Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate and improvise their weaknesses far before the actual elections. During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians. The amount of data generated is much large to study, analyze and draw inferences using the latest techniques. Twitter is one of the most popular social media platforms enables us to perform domain-specific data preparation. In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning. The tweets related to the Delhi Elections in 2020 are extracted and employed for the task. Among the several algorithms, we observe that Support Vector Machines portrays the best performance.
