Purpose

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

Design/methodology/approach

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Findings

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

Originality/value

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

中文翻译：

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预测社交媒体提要的评级：结合潜在因素和情感方面以提高不同分类器的性能

目的

各种社交平台的广泛接受增加了基于他们对服务的体验发布关于各种服务的用户数量。找出社交媒体 (SM) 帖子的预期评级对于组织和潜在用户都很重要，因为这些帖子可以帮助捕捉用户的观点。但是，与商家网站不同的是，与服务体验相关的 SM 帖子除非在评论中明确提及，否则无法评分。此外，预测评分还可以帮助使用最近的评论构建数据库，以在各种场景中测试推荐算法。

设计/方法/方法

在这项研究中，作者使用线性（朴素贝叶斯，最大熵）和非线性（k-最近邻，k-NN）分类器预测了 SM 帖子的评分，这些分类器利用不同特征、情感分数和情感分数的组合。

发现

总体而言，这项研究的结果表明，非线性分类器（k -NN 分类器）的性能优于线性分类器（朴素贝叶斯，最大熵分类器）。结果还显示了分类器与情绪和情绪分数相结合的性能改进。“重要因素”或“潜在因素”特征的引入也显示了分类器性能的提高。

原创性/价值

本研究通过使用机器学习算法以及情感方面和潜在因素等不同特征的组合，提供了一种预测 SM 提要评级的新途径。