We present a study of the affective character of 1st to 8th year Chilean school texts, to which we applied lexicon-based affect analysis techniques to identify 6 basic emotions (anger, sadness, fear, disgust, surprise and happiness). First, we generated a corpus of 525 documents, 18176 paragraphs and 137516 words. Then, using the affective words frequency, we built a classifier based on Emotion Word Density to detect emotions in the texts. Our results show that the predominant affective states are happiness (58%), sadness (16%) and fear (12%). The 6 basic emotions are present in most literary forms with uniform relative density except for songs, where anger is absent. Classifier performance was validated by comparing its results against the opinions of experts in the field, and its results show an above-average conformity (accuracy = 63%), above-average predictive capacity (precision = 69%) and good classifier sensitivity (recall = 80% and f-measure = 93%).