Natural Language Processing
Enables computers to hear human speech, recognize it, and determine sentiments in texts
Natural Language Processing (NLP) enables computers to hear human speech, recognize it, and read and determine sentiments in texts. The language is broken down into smaller parts, and the computer explores and understands how the relationships between the elements create meaning.
BENEFITS OF NATURAL LANGUAGE PROCESSING:
Plenty of natural-language data can be studied and structured
NLP is helpful in different fields with unstructured data – from Healthcare registrations to analysis of posts and comments in Social Media
It is used effectively for brand perception analysis and further development of marketing strategies
Many applications that make our lives easier – such as speech recognition apps or text analytics – rely on NLP to work
ILLUSTRATIVE EXAMPLES
NLP monitoring of Social Media for NATO StratCom
More than 92 million posts and millions of user profiles of VK, OK, and FB were studied to detect attempts to influence Russian-speaking citizens in the Baltic States through social networks. We used NLP to analyze ideological content and determined 11 macro topics such as Russia, USSR, West, Non-Citizens, and so on. Our conclusions were included in a publication entitled "Virtual Russian World in the Baltics".
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NLP in the case of banning VKontakte in Ukraine
We designed a study of 315,697 active Ukrainian VK user profiles to understand the effectiveness of government reactions to propaganda, such as a ban on VKontakte in Ukraine. With the help of NLP, we studied post topics and concluded that Pro-Russian propaganda notably increased, and the share of 'Ukrainian news' decreased, after the ban. The research became a part of a NATO StratCom study devoted to the role of governments in countering critical security challenges and was included in the publication "The Effects of Banning the Social Network VK in Ukraine".
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NLP in the case of the AB InBev
As a part of a marketing strategy improvement initiative for AB InBev, we analyzed more than 20,000 posts and comments and 6,000,000 profiles of potential customers on the Vkontakte and Facebook social networks. Using NLP, we identified users' associations with the brand's products. We identified some undesirable associations connected with promotional activities and football events. This insight could help improve future marketing promotions and brand positioning.
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NLP in bot detection
Before the Ukrainian elections, we studied 1 million VKontakte profiles and almost 10 million posts to understand what was going on and what people were saying about the nine most popular candidates. NLP was used to analyze language characteristics for further post clustering, and helped us identify 40 VKontakte users who seemed to be social bots.
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