ANEW for Spanish Twitter Sentiment Analysis Using Instance-Based Multi-label Learning Algorithms

• Published in: Information Management and Big Data Vol. 898; pp. 46 - 53
• Main Authors: Palomino, Rodrigo, Meléndez, Carlos, Mauricio, David, Valverde-Rebaza, Jorge
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Affective word lists; ANEW; Classification; Multi-label classification; Sentiment analysis; Twitter
• ISBN: 9783030116798; 3030116794
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-030-11680-4_6

In the last years, different efforts have been made to extract information that users express through online social networking services, e.g. Twitter. Despite the progress achieved, there are still open gaps to be addressed. Related to the sentiment analysis issue, we stand out the following gaps: (a) low accuracy in sentiment classification task for short texts; and, (b) lack of tools for sentiment analysis in several languages. Aiming to fill these gaps, in this paper we apply the Spanish adaptation of ANEW (Affective Norms for English Words) as resource to improve the Twitter sentiment analysis by applying a variety of multi-label classifiers in a corpus of Spanish tweets collected by us. To the best of our knowledge, this is the first work using a Spanish adaptation of ANEW for sentiment analysis.