TY - GEN
T1 - Aggregating news reporting sentiment by means of hesitant linguistic terms
AU - Nguyen, Jennifer
AU - Armisen, Albert
AU - Agell, N.
AU - Saz, Ángel
N1 - Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement n◦ 822654).
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy linguistic terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering linguistic terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant linguistic terms can be considered well suited for expressing the tone of articles.
AB - This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy linguistic terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering linguistic terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant linguistic terms can be considered well suited for expressing the tone of articles.
KW - Consensus measurement
KW - Hesitant fuzzy linguistic terms
KW - Sentiment analysis
UR - https://www.scopus.com/pages/publications/85090094537
U2 - 10.1007/978-3-030-57524-3_21
DO - 10.1007/978-3-030-57524-3_21
M3 - Conference contribution
AN - SCOPUS:85090094537
SN - 9783030575236
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 260
BT - Modeling Decisions for Artificial Intelligence
A2 - Torra, Vicenc
A2 - Narukawa, Yasuo
A2 - Nin, Jordi
A2 - Agell, Núria
PB - Springer
T2 - Modeling Decisions with Artificial Intelligence (MDAI 2020)
Y2 - 2 September 2020 through 5 September 2020
ER -