This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the purpose of improving the results of an affective analysis process, which is relevant to interpreting the textual information that is available in social networks. The hypothesis states that it is possible to improve affective analysis by using a lexicon that is enriched with the intensity values obtained from similarity metrics. Encouraging results were obtained when an affective analysis based on a labelled lexicon was compared with that based on another lexicon without intensity values.,The authors propose a method for the automatic extraction of the affective intensity values of words using the similarity metrics implemented in WS. First, the intensity values were calculated for words having an affective root in WordNet. Then, to evaluate the effectiveness of the proposal, the results of the affective analysis based on a labelled lexicon were compared to the results of an analysis with and without affective intensity values.,The main contribution of this research is a method for the automatic extraction of the intensity values of affective words used to enrich a lexicon compared with the manual labelling process. The results obtained from the affective analysis with the new lexicon are encouraging, as they provide a better performance than those achieved using a lexicon without affective intensity values.,Given the restrictions for calculating the similarity between two words, the lexicon labelled with intensity values is a subset of the original lexicon, which means that a large proportion of the words in the corpus are not labelled in the new lexicon.,The practical implications of this work include providing tools to improve the analysis of the feelings of the users of social networks. In particular, it is of interest to provide an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyberbullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.,This work is interested in providing an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyber bullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.,The originality of the research lies in the proposed method for automatically labelling the words of an affective lexicon with intensity values by using WS. To date, a lexicon labelled with intensity values has been constructed using the opinions of experts, but that method is more expensive and requires more time than other existing methods. On the other hand, the new method developed herein is applicable to larger lexicons, requires less time and facilitates automatic updating.

中文翻译：

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改进文本中的情感分析

本文旨在提出一种使用 WordNet Similarity (WS) 软件包自动标记具有强度值的情感词典的方法，目的是改进情感分析过程的结果，这与解释可用的文本信息有关。在社交网络中。该假设指出，可以通过使用富含从相似性度量获得的强度值的词典来改进情感分析。当基于标记词典的情感分析与基于另一个没有强度值的词典进行比较时，获得了令人鼓舞的结果。作者提出了一种使用 WS 中实现的相似性度量自动提取单词情感强度值的方法。第一的，强度值是针对 WordNet 中具有情感词根的词计算的。然后，为了评估提议的有效性，将基于标记词典的情感分析结果与有和没有情感强度值的分析结果进行比较。本研究的主要贡献是一种自动提取的方法与手动标记过程相比，用于丰富词典的情感词强度值的变化。使用新词典进行情感分析的结果令人鼓舞，因为它们比使用没有情感强度值的词典提供了更好的性能。,考虑到计算两个词之间相似度的限制，用强度值标记的词典是原始词典的一个子集，这意味着语料库中很大一部分词没有在新词典中标注。这项工作的实际意义包括提供工具来改进对社交网络用户感受的分析。特别是，提供一种情感词典来改进解决数字社会问题的尝试，例如网络欺凌的检测，是很有意义的。在这种情况下，通过在情绪检测方面实现更高的精度，可以检测网络欺凌情况下的参与者的角色，例如欺凌者和受害者。其他应用情感词汇很重要的问题是检测对妇女的攻击性或性别暴力，或检测年轻人和儿童的抑郁状态。这项工作有兴趣提供一个情感词典，以改善解决数字社会问题的尝试，例如检测网络欺凌。在这种情况下，通过在情绪检测中实现更高的精度，可以检测网络欺凌情况下的参与者的角色，例如欺凌者和受害者。情感词汇应用的其他重要问题是检测对妇女的攻击性或性别暴力或检测年轻人和儿童的抑郁状态。,该研究的独创性在于提出的自动标记单词的方法使用 WS 的具有强度值的情感词典。迄今为止，已经使用专家的意见构建了一个用强度值标记的词典，但与其他现有方法相比，该方法更昂贵且需要更多时间。另一方面，这里开发的新方法适用于更大的词典，需要更少的时间并且有利于自动更新。