[论文解读] Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)

Ali Nikseresht, Mohammad Hosein Raeisi+1 more|arXiv (Cornell University)|Sep 26, 2021

Digital Marketing and Social Media被引用 4

一句话总结

本研究提出了一种利用Twitter用户生成内容（UGC）进行情感分析的框架，以指导名人品牌决策，比较了基于词典的方法（朴素贝叶斯）与机器学习方法（朴素贝叶斯）的性能。研究发现，机器学习方法在情感分类中准确率更高，从而能够实现数据驱动的、更有效的名人代言人选定，以优化营销活动。

ABSTRACT

The volume of discussions concerning brands within social media provides digital marketers with great opportunities for tracking and analyzing the feelings and views of consumers toward brands, products, influencers, services, and ad campaigns in CGC. The present study aims to assess and compare the performance of firms and celebrities (i.e., influencers that with the experience of being in an ad campaign of those companies) with the automated sentiment analysis that was employed for CGC at social media while exploring the feeling of the consumers toward them to observe which influencer (of two for each company) had a closer effect with the corresponding corporation on consumer minds. For this purpose, several consumer tweets from the pages of brands and influencers were utilized to make a comparison of machine learning and lexicon-based approaches to the sentiment analysis through the Naive algorithm (lexicon-based) and Naive Bayes algorithm (machine learning method) and obtain the desired results to assess the campaigns. The findings suggested that the approaches were dissimilar in terms of accuracy; the machine learning method yielded higher accuracy. Finally, the results showed which influencer was more appropriate according to their existence in previous campaigns and helped choose the right influencer in the future for our company and have a better, more appropriate, and more efficient ad campaign subsequently. It is required to conduct further studies on the accuracy improvement of the sentiment classification. This approach should be employed for other social media CGC types. The results revealed decision-making for which sentiment analysis methods are the best approaches for the analysis of social media. It was also found that companies should be aware of their consumers' sentiments and choose the right person every time they think of a campaign.

研究动机与目标

• 评估并比较基于词典与机器学习方法在名人品牌决策中情感分析的性能。
• 基于社交媒体内容中的情感，识别哪位名人影响者对消费者的心理和情感影响更强，相较于品牌本身。
• 为营销人员提供可操作的见解，通过自动化的情感与极性分析，选择未来广告活动中的最优影响者。
• 评估社交媒体挖掘（SMM）与大数据分析在提升市场研究与品牌战略决策制定方面的有效性。

提出的方法

• 收集与品牌及其名人代言相关联的Twitter帖子中的消费者生成内容（CGC）。
• 应用两种情感分析技术：基于词典的方法（朴素算法）与机器学习方法（朴素贝叶斯算法）。
• 对推文进行极性和情感分析，将消费者情感分类为对品牌及其名人代言人的积极、消极或中性情感。
• 使用词云可视化技术识别CGC中与情感相关的关键术语与主题。
• 通过定量评估指标（由准确率对比隐含推断）比较两种情感分析方法的准确率与性能。
• 将研究发现整合至决策模型中，以指导未来的影响者选择与活动优化。

实验结果

研究问题

• RQ1 在Twitter上对品牌与名人进行情感分类时，基于词典的方法与机器学习方法中，哪种方法的准确率更高？
• RQ2 消费者对品牌的看法与对名人代言人的看法相比如何？哪一个实体更能引发强烈的情感反应？
• RQ3 在每种品牌对应的两位名人影响者中，哪一位在消费者中产生了更显著的心理影响，依据CGC中的情感模式判断？
• RQ4 社交媒体内容的情感分析在多大程度上能够为名人品牌与市场研究的战略决策提供支持？

主要发现

• 基于机器学习的情感分析（朴素贝叶斯算法）在分类Twitter UGC中的消费者情感时，准确率高于基于词典的方法。
• 消费者对名人代言人的态度被发现是品牌认知与活动有效性的重要预测指标。
• 本研究识别出特定影响者在受众中具有更强的情感共鸣，从而可实现基于数据的未来活动人选决策。
• 将情感分析与社交媒体挖掘相结合，提供了可操作的见解，有助于优化营销策略、降低活动风险并改善资源分配。
• 最终的决策模型表明，基于情感的洞察能够增强市场研究中的竞争优势与战略规划。
• 结果表明，企业应持续监控消费者情感，并利用自动化情感分析，以选择最具影响力的代言人用于品牌建设活动。