Research Outputs
Is news really pessimistic? Sentiment Analysis of Chilean online newspaper headlines
Objectives: This paper explores the popular belief that all news is bad news. Many claim not to read newspapers to avoid knowing about the worst of our society. We want tear down the myth by applying a Sentiment Analysis (SA) approach. Method/Analysis: This work applies sentiment analysis techniques to study the headline bias of online newspapers for the period between March 2014 and April 2015. We analyzed 2953 headlines gathered from five of the most popular Chilean newspapers which are available online and offer RSS feeds. Findings: Our results show a roughly equivalent percentage of positive bias (38%) and negative bias (37%) instances, with 25% of headlines exhibiting a neutral bias. Automatic classification performance is promising, with decent classifier performance and sensitivity, with plenty of room for improvement. Novelty/Improvement: This work also a domain-specific Spanish language tagged corpus was generated as a result of this work, which is a valuable resource for future studies.
What do our Children read about? Affect analysis of Chilean school texts
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%).