6533b85cfe1ef96bd12bd020

RESEARCH PRODUCT

Tecnhiques for sentiment analysis in Twitter: Supervised Learning and SentiStrength

Tomás Baviera

subject

Aprendizaje automático supervisadoSentiment analysisUNESCO::CIENCIAS DE LAS ARTES Y LAS LETRASAnálisis de sentimientoSentiStrengthPolitical communicationTwitter:CIENCIAS DE LAS ARTES Y LAS LETRAS [UNESCO]Comunicación políticaCOMERCIALIZACION E INVESTIGACION DE MERCADOSSupervised learning

description

[EN] Sentiment analysis on Twitter offers possibilities of great interest to evaluate the currents of opinion disseminated through this medium. The huge volumes of texts require tools able to automatically process these messages without losing reliability. This paper describes two different types of approaching this problem. The first strategy is based on Supervised Learning processes, developed in the field of artificial intelligence. Its application requires some tools from natural language processing along with a classifed corpus as a starting point. The second approach is based on polarity dictionaries. SentiStrength tool is located in this line. It is increasingly applied to studies of Twitter in English. The paper assesses the most advanced studies using each of these approaches for analyzing tweets in Spanish. Finally, the advantages and limitations of each of these approaches for researching political communication are assessed. While supervised learning allows taking into account the context thanks to its ability to detect patterns of words, the researcher who uses this approach requires having data analyst skills to better refine the process. Instead, SentiStrength is more oriented to the semantic content of the terms of the message. It requires more of a competence in linguistics by the researcher. The main conclusion of this study is that both automated methods of analysis can not do without a demanding manual coding if they are to be used reliably in research.

yearjournalcountryeditionlanguage
2017-01-01
http://hdl.handle.net/10550/59501