Purpose

This paper aims to describe the process used to create an emotion lexicon enriched with the emotional intensity of words and focuses on improving the emotion analysis process in texts.

Design/methodology/approach

The process includes setting, preparation and labelling stages. In the first stage, a lexicon is selected. It must include a translation to the target language and labelling according to Plutchik’s eight emotions. The second stage starts with the validation of the translations. Then, it is expanded with the synonyms of the emotion synsets of each word. In the labelling stage, the similarity of words is calculated and displayed using WordNet similarity.

Findings

The authors’ approach shows better performance to identification of the predominant emotion for the selected corpus. The most relevant is the improvement obtained in the results of the emotion analysis in a hybrid approach compared to the results obtained in a purist approach.

Research limitations/implications

The proposed lexicon can still be enriched by incorporating elements such as emojis, idioms and colloquial expressions.

Practical implications

This work is part of a research project that aids in solving problems in a digital society, such as detecting cyberbullying, abusive language and gender violence in texts or exercising parental control. Detection of depressive states in young people and children is added.

Originality/value

This semi-automatic process can be applied to any language to generate an emotion lexicon. This resource will be available in a software tool that implements a crowdsourcing strategy allowing the intensity to be re-labelled and new words to be automatically incorporated into the lexicon.

中文翻译：

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创建标签词典以改善文本情感分析的新颖方法

目的

本文旨在描述用于创建情感词典的过程，该词典丰富了单词的情感强度，并着重于改进文本中的情感分析过程。

设计/方法/方法

该过程包括设置，准备和标记阶段。在第一阶段，选择一个词典。它必须包括针对目标语言的翻译，并根据Plutchik的八种情感进行标注。第二阶段从翻译验证开始。然后，使用每个单词的情感同义词集的同义词对其进行扩展。在标注阶段，使用WordNet相似度计算并显示单词的相似度。

发现

作者的方法显示出更好的性能来识别所选语料库的主要情绪。与最纯粹的方法相比，最相关的是混合方法在情感分析结果中获得的改进。

研究局限/意义

通过结合诸如表情符号，成语和口语表达之类的元素，仍可以丰富所提议的词典。

实际影响

这项工作是一项研究项目的一部分，该研究项目有助于解决数字社会中的问题，例如检测文本中的网络欺凌，辱骂性语言和性别暴力或行使父母的控制权。增加了对年轻人和儿童的抑郁状态的检测。

创意/价值

该半自动过程可以应用于任何语言，以生成情感词典。该资源将在实施众包策略的软件工具中提供，该策略允许重新标记强度并将新单词自动合并到词典中。