![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/15db2e3254ffa460ce7209d14ca04c1e-360x270.jpg\")
<\/p>\n<\/p>\n
Quantitative text analysis is a technique that uses computers to process written or spoken words, or \u201cnatural language,\u201d to extract information for the social sciences and humanities. In political communication research, which is my field of study, quantitative text analysis is used for many applications, including inferring the political ideologies of candidates through analyzing election manifestos and politicians\u2019 speeches, or uncovering concealed political bias in newspaper articles. Quantitative text analysis is also used for tasks such as classifying books according to themes and, in a different kind of application, searching for the authors of anonymously published literary works.<\/p>\n
Research on natural language processing began with research into automated translation as an application of decryption technology during the Second World War. In the 1990s, the technology advanced dramatically due to improvements in computer performance in conjunction with the increase in the availability of electronic data and emergence of the Internet; thus, statistical analysis techniques for large-scale text data were established. From the early 2000s to the present day, a variety of machine learning models that can be used for quantitative text analysis have been developed.<\/p>\n
I obtained a Ph.D. from the London School of Economics and Political Science (LSE), and subsequently worked there as a Research Officer before returning to Japan in 2018. During that period, I was and currently remain involved in the development of Quanteda<\/a> (an abbreviation of Quantitative Analysis of Textual Data), a text analysis package written with the programming language R. Using Quanteda, I analyzed how other countries\u2019 perceptions of the United States have correlated with the nation’s domestic political situation over the past 30 years through newspaper articles published in Japan and Britain. <\/p>\n When the measured values are averaged for each term of presidents Reagan (1981 \u2013 1988), Bush (H.) (1989 \u2013 1993), Clinton (1993 \u2013 2001), Bush (W.) (2001 \u2013 2009), Obama (2009 \u2013 2017), and Trump (2017 \u2013 ), it is clear that perceptions of the United States have changed over a 30-year period.<\/p>\n <\/p>\n Another example of research using Quanteda is its application in analyzing articles from the state-controlled Russian website Sputnik News (https:\/\/sputniknews.com\/<\/a>). Sputnik News has editorial departments in more than 30 languages, including Japanese, English, Spanish, French, and German.
\nQuanteda is a tool for quantitative text analysis that Kenneth Benoit from the LSE began developing in 2012 with support from the European Research Council, and Version 1.0 was announced in January 2018. A variety of software has been developed for text analysis to date, but no multifunctional and efficient software has yet been developed.
\nEven at its present stage, Quanteda is more multifunctional than tm and tidytext, the previous R packages, and gensim, a Python package. It is also superior in terms of processing speed and memory usage. It is easy for researchers in humanities to use and has gained the support of leading political scientists in North America and Europe.
\nIt is further differentiated by its ability to support Asian languages such as Chinese, Japanese, and Korean due to its compatibility with Unicode, which can handle all languages. Using the same analytic tools and models as English-speaking researchers, it is possible to make international comparisons and publish in leading journals. I think that researchers in Asia, at least those in Japan, have great expectations of Quanteda.<\/p>\n![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/d2a2527386ba3abfc7d314f5b582e3cd-610x409.jpg\")
Analyzing Changes in Other Countries\u2019 Perceptions of the United States from Newspaper Articles<\/h3>\n
\nI performed a keyword search on a database of Japanese and British articles (using the terms \u201cUnited States\u201d OR \u201cUS\u201d AND \u201cgovernment\u201d OR \u201cpolitics\u201d OR \u201cdiplomacy\u201d OR \u201cmilitary\u201d\uff09and used a semi-supervised vector space model \uff08Latent Semantic Analysis<\/a>\uff09to quantitatively analyze how the 141,746 articles extracted from The Asahi Shimbun<\/i> and the 176,399 articles extracted from The Guardian<\/i> covered the United States from the perspective of framing.<\/p>\n![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/5ddbd2f0dde3151c66238e0c645264ca-610x298.jpg\")
![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/efee1ed0bb1ea61d77eb5d917c387e08-610x297.jpg\")
![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/cd2d5dfcfeb006e904abace87c055eca-940x846.jpg\")
![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/8c2afc83c445cf4613d845c6cd9bef9c-940x846.jpg\")
Analyzing Russia\u2019s International Internet Propaganda Strategy<\/h3>\n
\nI classified the 51,651 English-language articles that have appeared on the website since July 2017 into six topics, economy, politics, society, diplomacy, military, and nature, and extracted the following keywords from each topic.<\/p>\n
![\"\"](\"https:\/\/www.waseda.jp\/inst\/wias\/assets\/uploads\/2018\/11\/3524272b960bd3815fc73f3420561240-610x408.jpg\")