9/21/2023 0 Comments Strong negative words![]() ![]() This leads to the conclusion that positive words carry less information than negative ones. ![]() Furthermore, we extend the analysis of information content by taking into account word context rather than just word frequency. This points towards an emotional bias in used language and supports Pollyanna hypothesis, which asserts that there is a bias towards the usage of positive words. While the rational process that optimizes communication determines word lengths by the information they carry, we find that the emotional content affects the word frequency such that positive words appear more frequently. This way, we reveal the importance of emotional content in human communication which influences the information carried by words. In order to link the emotionality of each word with the information it carries, we build on the recent work of Piantadosi et al. These languages are used everyday by more than 805 million users, who create the majority of the content available on the Internet. We provide a study of the baseline of written emotional expression on the Internet in three languages that span more than 67.7% of the websites : English (56.6%), German (6.5%), and Spanish (4.6%). Our investigation is devoted to this problem by combining two analyses, (i) quantifying the emotional content of words in terms of valence, and (ii) quantifying the frequency of word usage in the whole indexable web. Thus, for all researchers dealing with emotions in written text it would be of particular importance to know about such bias, how it can be quantified, and how it affects the baseline, or reference point, for expressed emotions. Recent studies have provided statistical analyses and modelling approaches of individual and collective emotions on the Internet.Īn emotional bias in written expressions, however, would have a strong impact, as it shifts the balance between positive and negative expressions. Sentiment analysis techniques allow to quantify the emotions expressed through posts and messages. Millions of individuals write text online, for which a quantitative analysis can provide new insights into the structure of human language and even provide a validation of social theories. This approach requires additional data beyond word length and frequency, which became available thanks to large datasets of human behaviour on the Internet. Our work focuses on one particular aspect of meaning, namely the emotion expressed in a word, and how this is related to word frequency and information content. Further discussions highlighted the relevance of meaning as part of the communication process as, for example, more abstract ideas are expressed through longer words. extended Zipf’s approach by showing that, in order to have efficient communication, word length increases with information content. Zipf’s law highlighted fundamental principles of organization in human language, and called for an interdisciplinary approach to understand its origin and its relation to word meaning. Historically, the frequency of words was first analyzed by Zipf showing that frequency predicts the length of a word as result of a principle of least effort. Considering, however, the everyday usage frequency of these words we find that the overall emotion of the three languages is strongly biased towards positive values, because words associated with a positive emotion are more frequently used than those associated with a negative emotion. ![]() The emotional content averaged over all the words in each of them is neutral. These lexica cover three of the most used languages on the Internet, namely English, German, and Spanish. We have tested and measured this bias in the context of online written communication by analyzing three established lexica of affective word usage. ![]() This question becomes particularly relevant for sentiment classification, as many tools assume as null hypothesis that human expression has neutral emotional content, or reweight positive and negative emotions without a quantification of the positive bias of emotional expression. If the type of diction you have in mind isn’t represented in one of the lists above, consider one of the following options.One would argue that human languages, in order to facilitate social relations, should be biased towards positive emotions. The degree of formality isn’t the only defining characteristic for diction. ![]()
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