Using Simple Algorithms for Social Media Sentiment Analysis: Analyzing User Emotions on Social Platforms

Abstract

Under the background of the vigorous development of social media, it is very important for business decision-making and social governance to accurately capture and analyze users' emotions. This article aims to explore a simple and effective way to analyze users' emotions on social media by using the basic NLP (Natural Language Processing) technology, especially the word frequency statistics method. Taking Weibo as the data source, this article constructs an emotional analysis model. By collecting data, preprocessing, constructing an emotion dictionary and calculating the emotion score, the model realizes the emotion tendency recognition of Weibo's posts. The experimental results show that the model can accurately identify the dominant emotional tendency in Weibo's posts, and perform well in capturing emotional trends. The research in this article not only provides a feasible solution for the emotional analysis of social media, but also provides reference for the follow-up research.