Abstract

Sentiment analysis (SA) is an essential task for many domains where it is crucial to know users’ public opinion about events, products, brands, politicians and so on. Existing works on SA have concentrated on English texts including Twitter feeds and user reviews on hotels, movies and products. On the other hand, Facebook, as an online social network (OSN), has attracted quite limited attention from the research community. Among these, SA work on Turkish text obtained from OSNs are extremely scarce. In this paper, our aim is to perform SA on public Facebook data collected from Turkish user accounts. Our study differs from existing studies in terms of the data set scale, the natural language of the texts in the data set and the extent of experimental analyses that include both machine learning and deep learning techniques. We extensively report not only the results of different learning models involving SA but also statistical distribution of metadata of user activities across various user attributes (e.g. gender and age). Our experimental results indicate that recurrent neural networks achieve the best accuracy (i.e. 0.916) with word embeddings. To the best of our knowledge, this is the best result for SA on Facebook data in the context of the Turkish language.

中文翻译：

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基于深度学习的Facebook数据情感分析：以土耳其用户为例

摘要

情感分析（SA）是许多领域的重要任务，在这些领域中，了解用户对事件，产品，品牌，政客等的公众舆论至关重要。SA的现有作品主要集中在英文文本上，包括Twitter提要以及酒店，电影和产品上的用户评论。另一方面，作为在线社交网络（OSN）的Facebook受到研究界的关注非常有限。其中，SA对从OSN获得的土耳其语文本的工作极为匮乏。在本文中，我们的目标是对从土耳其用户帐户收集的公共Facebook数据执行SA。我们的研究在数据集规模，数据集中文本的自然语言以及包括机器学习和深度学习技术在内的实验分析的范围方面与现有研究有所不同。我们不仅广泛报告涉及SA的不同学习模型的结果，而且还报告跨各种用户属性（例如性别和年龄）的用户活动的元数据的统计分布。我们的实验结果表明，递归神经网络通过词嵌入实现了最佳精度（即0.916）。据我们所知，这是在土耳其语语境下SA在Facebook数据上取得的最佳结果。