Affective News: The Automated Coding of Sentiment in Political Texts

被引:373
作者
Young, Lori [2 ]
Soroka, Stuart [1 ]
机构
[1] McGill Univ, Dept Polit Sci, Montreal, PQ H3A 2T7, Canada
[2] Univ Penn, Annenberg Sch Commun, Philadelphia, PA 19104 USA
关键词
content analysis; media tone; methodology; PSYCHOTHERAPEUTIC PROCESSES; SEMANTIC ORIENTATION; ECONOMIC-NEWS; INFORMATION; WORDS; NARRATIVES; KNOWLEDGE; LANGUAGE; EMOTION; VALENCE;
D O I
10.1080/10584609.2012.671234
中图分类号
G2 [信息与知识传播];
学科分类号
050302 [传播学];
摘要
An increasing number of studies in political communication focus on the "sentiment" or "tone" of news content, political speeches, or advertisements. This growing interest in measuring sentiment coincides with a dramatic increase in the volume of digitized information. Computer automation has a great deal of potential in this new media environment. The objective here is to outline and validate a new automated measurement instrument for sentiment analysis in political texts. Our instrument uses a dictionary-based approach consisting of a simple word count of the frequency of keywords in a text from a predefined dictionary. The design of the freely available Lexicoder Sentiment Dictionary (LSD) is discussed in detail here. The dictionary is tested against a body of human-coded news content, and the resulting codes are also compared to results from nine existing content-analytic dictionaries. Analyses suggest that the LSD produces results that are more systematically related to human coding than are results based on the other available dictionaries. The LSD is thus a useful starting point for a revived discussion about dictionary construction and validation in sentiment analysis for political communication.
引用
收藏
页码:205 / 231
页数:27
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