Aggregating News Reporting Sentiment by Means of Hesitant Linguistic Terms.

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.