论文标题
多产品社交媒体平台的情感分析比较研究
Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms
论文作者
论文摘要
由于当前世界的快速增长，每秒生成大量数据。该研究领域试图确定人们在社交媒体帖子上的感受或观点。我们使用的数据集是从Twitter，Reddit等各种社交网站的评论部分中的多源数据集。使用自然语言处理技术来对所获得的数据集进行情感分析。在本文中，我们使用基于词典的机器学习和深度学习方法的技术提供了比较分析。这项工作中使用的机器学习算法是天真的贝叶斯，这项工作中使用的基于词典的方法是TextBlob，这项工作中使用的深度学习算法是LSTM。
There is a vast amount of data generated every second due to the rapidly growing technology in the current world. This area of research attempts to determine the feelings or opinions of people on social media posts. The dataset we used was a multi-source dataset from the comment section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were employed to perform sentiment analysis on the obtained dataset. In this paper, we provide a comparative analysis using techniques of lexicon-based, machine learning and deep learning approaches. The Machine Learning algorithm used in this work is Naive Bayes, the Lexicon-based approach used in this work is TextBlob, and the deep-learning algorithm used in this work is LSTM.
