[论文解读] Sentiment Analysis on Social Media Content

Antony Samuels, John Mcgonical|ArXiv.org|Jul 4, 2020

Sentiment Analysis and Opinion Mining参考文献 8被引用 5

一句话总结

本文提出了一种混合情感分析模型，结合了监督式与非监督式机器学习，以将Twitter推文分类为正面、负面或中性情感。该模型在真实社交媒体数据上表现出色，通过交叉验证和F1分数指标，在分析麦当劳与肯德基公众情感时实现了高准确率。

ABSTRACT

Nowadays, people from all around the world use social media sites to share information. Twitter for example is a platform in which users send, read posts known as tweets and interact with different communities. Users share their daily lives, post their opinions on everything such as brands and places. Companies can benefit from this massive platform by collecting data related to opinions on them. The aim of this paper is to present a model that can perform sentiment analysis of real data collected from Twitter. Data in Twitter is highly unstructured which makes it difficult to analyze. However, our proposed model is different from prior work in this field because it combined the use of supervised and unsupervised machine learning algorithms. The process of performing sentiment analysis as follows: Tweet extracted directly from Twitter API, then cleaning and discovery of data performed. After that the data were fed into several models for the purpose of training. Each tweet extracted classified based on its sentiment whether it is a positive, negative or neutral. Data were collected on two subjects McDonalds and KFC to show which restaurant has more popularity. Different machine learning algorithms were used. The result from these models were tested using various testing metrics like cross validation and f-score. Moreover, our model demonstrates strong performance on mining texts extracted directly from Twitter.

研究动机与目标

• 开发一种能够处理非结构化Twitter数据的稳健情感分析模型。
• 评估结合监督式与非监督式学习技术进行情感分类的有效性。
• 利用实时Twitter数据，分析公众对两大快餐连锁品牌麦当劳与肯德基的情感。
• 通过交叉验证和F1分数等标准指标衡量模型性能。
• 展示情感分析在品牌监控与公众认知追踪方面的实际应用价值。

提出的方法

• 使用Twitter API提取与麦当劳和肯德基相关的实时推文。
• 文本预处理步骤包括数据清洗与特征发现，以应对非结构化的社交媒体内容。
• 在预处理后的数据上训练多种机器学习算法，用于情感分类。
• 该模型结合了监督式与非监督式学习方法，以提升分类的鲁棒性。
• 通过交叉验证与F1分数指标评估性能，以确保结果的可靠性与泛化能力。
• 根据模型预测结果，为每条推文分配情感标签（正面、负面、中性）。

实验结果

研究问题

• RQ1 结合监督式与非监督式学习的混合方法在Twitter情感分析中的有效性如何？
• RQ2 所提出的模型能否准确地将现实世界中的Twitter数据分类为正面、负面或中性情感？
• RQ3 基于社交媒体提及，公众对麦当劳与肯德基的情感对比如何？
• RQ4 F1分数与交叉验证等标准评估指标在评估社交媒体文本模型可靠性方面表现如何？
• RQ5 该模型在实时品牌情感监控中的适用程度如何？

主要发现

• 混合模型在将推文分类为正面、负面或中性情感类别方面表现出色。
• 交叉验证与F1分数指标证实了该模型在Twitter数据上的可靠性与泛化能力。
• 该模型成功利用实时社交媒体内容捕捉了麦当劳与肯德基的公众情感趋势。
• 监督式与非监督式学习的整合提升了分类准确率，优于传统方法。
• 结果表明，利用机器学习对非结构化社交媒体平台进行大规模情感分析具有可行性。
• 本研究为公司通过社交媒体情感监控品牌认知提供了实用框架。