Abstract
In this project we propose a new approach for emotion recognition using web-based similarity (e.g. confidence, PMI and PMING). We aim to extract basic emotions from short sentences with emotional content (e.g. news titles, tweets, captions), performing a web-based quantitative evaluation of semantic proximity between each word of the analyzed sentence and each emotion of a psychological model (e.g. Plutchik, Ekman, Lovheim). The phases of the extraction include: text preprocessing (tokenization, stop words, filtering), search engine automated query, HTML parsing of results (i.e. scraping), estimation of semantic proximity, ranking of emotions according to proximity measures. The main idea is that, since it is possible to generalize semantic similarity under the assumption that similar concepts co-occur in documents indexed in search engines, therefore also emotions can be generalized in the same way, through tags or terms that express them in a particular language, ranking emotions. Training results are compared to human evaluation, then additional comparative tests on results are performed, both for the global ranking correlation (e.g. Kendall, Spearman, Pearson) both for the evaluation of the emotion linked to each single word. Different from sentiment analysis, our approach works at a deeper level of abstraction, aiming at recognizing specific emotions and not only the positive/negative sentiment, in order to predict emotions as semantic data.
Abstract (translated by Google)
在这个项目中，我们提出了一种基于网络的相似性（例如置信度，PMI和PMING）的情感识别新方法。我们的目的是从情感内容的简短句子（例如新闻标题，推文，标题）中提取基本情绪，对分析的句子的每个单词与心理模型的每种情绪之间的语义接近度进行基于网络的定量评估（例如Plutchik， Ekman，Lovheim）。提取阶段包括：文本预处理（标记化，停用词，过滤），搜索引擎自动查询，结果（即刮取）的HTML解析，语义接近度的估计，根据接近度量度的情绪排序。其主要思想是，由于在搜索引擎索引的文档中类似概念同时出现的假设下，可以推广语义相似性，所以也可以通过以同样方式概括情感，通过标签或术语表达它们特定的语言，排名的情绪。将训练结果与人类评估进行比较，然后对全球排名相关性（例如肯德尔（Kendall），斯皮尔曼（Spearman），皮尔逊）进行额外的结果比较测试，以评估与每个单词有关的情绪。与情感分析不同，我们的方法在更深层次的抽象层面上工作，旨在识别特定的情绪，而不仅仅是正面/负面的情绪，以便将情绪预测作为语义数据。
URL
https://arxiv.org/abs/1612.05734