Abstract
In this thesis, we developed a comprehensive framework for sentiment analysis that takes its many aspects into account mainly for Turkish. We have also proposed several approaches specific to sentiment analysis in English only. We have accordingly made five major and three minor contributions. We generated a novel and effective feature set by combining unsupervised, semi-supervised, and supervised metrics. We then fed them as input into classical machine learning methods, and outperformed neural network models for datasets of different genres in both Turkish and English. We created a polarity lexicon with a semi-supervised domain-specific method, which has been the first approach applied for corpora in Turkish. We performed a fine morphological analysis for the sentiment classification task in Turkish by determining the polarities of morphemes. This can be adapted to other morphologically-rich or agglutinative languages as well. We have built a novel neural network architecture, which combines recurrent and recursive neural network models for English. We built novel word embeddings that exploit sentiment, syntactic, semantic, and lexical characteristics for both Turkish and English. We also redefined context windows as subclauses in modelling word representations in English. This can also be applied to other linguistic fields and natural language processing tasks. We have achieved state-of-the-art and significant results for all these original approaches. Our minor contributions include methods related to aspect-based sentiment in Turkish, parameter redefinition in the semi-supervised approach, and aspect term extraction techniques for English. This thesis can be considered the most detailed and comprehensive study made on sentiment analysis in Turkish as of July, 2020. Our work has also contributed to the opinion classification problem in English.
Abstract (translated)
在这篇论文中，我们开发了一个全面的框架用于情感分析，该框架充分考虑了情感分析的各种方面，主要针对土耳其语。此外，我们也提出了一些仅适用于英语的情感分析特定方法。根据这些研究，我们做出了五项重大贡献和三项次要贡献。 1. 我们生成了一套新颖且有效的特征集合，通过结合无监督、半监督和有监督度量来实现。 2. 随后将这些特征作为输入传递给传统的机器学习方法，并在土耳其语和英语不同类型的语料库中超过了神经网络模型的表现。 3. 利用一种半监督领域的特定方法创建了极性词典，这是首次应用于土耳其语语料库的方法。 4. 对于土耳其语的情感分类任务进行了精细的形态学分析，确定了词缀的情感极性。这一方法也可以适应其他富形态或黏着语系的语言。 5. 我们构建了一种新型神经网络架构，将循环和递归神经网络模型相结合用于英语。 此外，我们还创建了针对土耳其语和英语的新颖词汇嵌入技术，这些技术利用情感、句法、语义及词汇特性。另外，我们在构建英语单词表示时重新定义了上下文窗口为子句结构，该方法同样适用于其他语言学领域和自然语言处理任务。 对于所有上述原创方法，我们取得了最先进的成果，并具有显著意义。次要贡献包括土耳其语基于方面的情感分析方法、半监督方法中的参数重定义以及英语的方面术语提取技术。 截至2020年7月，这篇论文可被视为对土耳其语情感分析进行最详细和全面研究的工作之一。我们的工作还为英语的意见分类问题做出了贡献。
URL
https://arxiv.org/abs/2512.00515
