This is probably the final version of my immigration dictionary. This text analysis dictionary was created using technique called the Latent Semantic Scaling, which is based on the Latent Semantic Analysis, from British newspaper corpus.
The result of the automated content analysis by this dictionary is strongly corresponds to manual coding by Amazon’s Mechanical Turks as you can see in the chart (whiskers represent 95% confidence intervals). Yet, please note that the documents coded by the dictionary are only sentences about immigration in the party manifestos selected by keywords (‘immigra*’, ‘migra*’, ‘refugee*’, ‘asylum*’, ‘foreign*’).
The dictionary is made up of 750 entry words. The following is the top 30 most positive and negative words in the dictionary. Many of them are intuitively positive or negative, but some are not. For example, ‘globalisation’ is positive only in the context of immigration. This is why texts are restricted to sentences on this subject. We can spot words like ‘species’ and ‘wildebeest’, because the newspaper corpus contains stories about animal migration, but it is not too harmful.
# Positive words 1 skills 100 2 globalisation 88.24 3 chauffeured 86.93 4 airport 86.68 5 ranging 82.41 6 clearance 79.48 7 status 78.4 8 agency 74.98 9 issues 72.15 10 breed 69.45 11 claimed 68.84 12 vehemently 68.6 13 skill 67.3 14 test 65.91 15 attract 64.39 16 permanent 63.68 17 legal 59.23 18 melting-pot 57.34 19 species 57.27 20 wildebeest 56.96 21 overstaying 56.07 22 documents 55.9 23 routes 55.75 24 work 55.63 25 shambles 55.28 26 breeding 53.65 27 bringing 53.24 28 employ 52.76 29 passport 52.24 30 official 51.88 # Negative words 1 xenophobia -141.27 2 control -130.09 3 racist -125.2 4 stemming -122.5 5 tide -122.46 6 working-class -115.53 7 negative -113.76 8 failure -110.32 9 problems -106.95 10 influx -100.81 11 branded -99.42 12 caused -96.82 13 exploit -94.11 14 first-generation -90.78 15 warned -89.93 16 families -88.51 17 soaring -86.53 18 ignored -86.45 19 housed -85.33 20 magnet -84.47 21 borders -83.18 22 newly-arrived -83.12 23 accused -82.89 24 evicted -82.02 25 trickle -81.42 26 rates -79.42 27 fuelled -78.34 28 flooded -76.69 29 non-white -76.48 30 lorries -76.38
