Understanding politics with Watson using Text Analytics

To understand the topics that actually are important to different political parties is a difficult task. Can text analytics together with an search index be an approach to given a better understanding?

This blog post describes how IBM Watson Explorer Content Analytics (WCA) can be used to make sense of Swedish politics. All speeches (in Swedish: anföranden) in the Swedish Parliament from 2004 to 2015 are analyzed using WCA. In total 139 110 transcribed text documents were analyzed. The Swedish language support build by Findwise for WCA is used together with a few text analytic processing steps which parses out person names, political party, dates and topics of interest. The selected topics in this analyzed are all related to infrastructure and different types of fuels.

We start by looking at how some of the topics are mentioned over time.

Analyze of terms of interets in Swedsih parlament between 2004 and 2014.

Analyze of terms of interest in Swedish parliament between 2004 and 2014.

The view shows topic which has a higher number of mentions compared to what would be expected during one year. Here we can see among other topics that the topic flygplats (airport) has a high increase in number of mentioning during 2014.

So let’s dive down and see what is being said about the topic flygplats during 2014.

Swedish political parties mentioning Bromma Airport.

Swedish political parties mentioning Bromma Airport during 2014.

The above image shows how the different political parties are mentioning the topic flygplats during the year 2014. The blue bar shows the number of times the topic flygplats was mentioned by each political party during the year. The green bar shows the WCA correlation value which indicates how strongly related a term is to the current filter. What we can conclude is that party Moderaterna mentioned flygplats during 2014 more frequently than other parties.

Reviewing the most correlated nouns when filtering on flygplats and the year 2014 shows among some other nouns: Bromma (place in Sweden), airport and nedläggning (closing). This gives some idea what was discussed during the period. By filtering on the speeches which was held by Moderaterna and reading some of them makes it clear that Moderaterna is against a closing of Bromma airport.

The text analytics and the index provided by WCA helps us both discover trending topics over time and gives us a tool for understanding who talked about a subject and what was said.

All the different topics about infrastructure can together create a single topic for infrastructure. Speeches that are mentioning tåg (train), bredband (broadband) or any other defined term for infrastructure are also tagged with the topic infrastructure. This wider concept of infrastructure can of course also be viewed over time.

Discussions in Swedish parliament mentioning the defined terms which builds up the subject infrastructure 2004 to 2015.

Discussions in Swedish parliament mentioning the defined terms which builds up the subject infrastructure 2004 to 2015.

Another way of finding which party that are most correlated to a subject is by comparing pair of facets. The following table shows parties highly related to terms regarding infrastructure and type of fuels.

Political parties highly correlated to subjects regarding infrastructure and types of fuel.

Swedish political parties highly correlated to subjects regarding infrastructure and types of fuel.

Let’s start by explain the first row in order to understand the table. Mobilnät (mobile net) has only been mentioned 44 times by Centerpartiet, but Centerpartiet is still highly related to the term with a WCA correlation value of 3.7. This means that Centerpartiet has a higher share of its speeches mentioning mobilnät compared to other parties. The table indicates that two parties Centerpartiet and Miljöpartiet are more involved about the subject infrastructure topics than other political parties.

Swedish parties mentioning the defined concept of infrastructure.

Swedish parties mentioning the defined concept of infrastructure.

Filtering on the concept infrastructure also shows that Miljöpartiet and Centerpartiet are the two parties which has the highest share of speeches mentioning the defined infrastructure topics.

Interested to dig deeper into the data? Parsing written text with text analytics is a successful approach for increasing an understanding of subjects such as politics. Using IBM Watson Explorer Content Analytics makes it easy. Most of the functionality used in this example is also out of the box functionalities in WCA.

Swedish language support (natural language processing) for IBM Content Analytics (ICA)

Findwise has now extended the NLP (natural language processing) in ICA to include both support for Swedish PoS tagging and Swedish sentiment analysis.

IBM Content Analytics with Enterprise Search (ICA) has its strength in natural language processing (NLP) which is achieved in the UIMA pipeline. From a Swedish perspective, one concern with ICA has always been its lack of NLP for Swedish. Previously the Swedish support in ICA consisted only of dictionary-based lemmatization (word: “sprang” -> lemma: “springa”). However, for a number of other languages ICA has also provided part of speech (PoS) tagging and sentiment analysis. One of the benefits of the PoS tagger is its ability to disambiguate words, which belong to multiple classes (e.g. “run” can be both a noun and a verb) as well as assign tags to words, which are not found in the dictionary. Furthermore, the POS tagger is crucial when it comes to improving entity extraction, which is important when a deeper understanding of the indexed text is needed.

Findwise has now extended the NLP in ICA to include both support for Swedish PoS tagging and Swedish sentiment analysis. The two images below shows simple examples of the PoS support.

Example when ICA uses NLP to analyse the string "ICA är en produkt som klarar entitetsextrahering"Example when ICA uses NLP to analyse the string "Watson deltog i jeopardy"

The question is how this extended functionality could be used?

IBM uses ICA and its NLP support together with several of their products. The jeopardy playing computer Watson may be the most famous example, even if it is not a real product. Watson used NLP in its UIMA pipeline when it analyzed its data from sources such as Wikipedia and Imdb.

One product which leverage from ICA and its NLP capabilities is Content and Predictive Analytics for Healthcare. This product helps doctors to determine which action to take for a patient given the patient’s journal and the symptoms. By also leveraging the predictive analytics from SPSS it is possible to suggest the next action for the patient.

ICA can also be connected directly to IBM Cognos or SPSS where ICA is the tool which creates structure to unstructured data. By using the NLP or sentiment analytics in ICA, structured data can be extracted from text documents. This data can then be fed to IBM Cognos, SPSS or non IBM products such as Splunk.

ICA can also be used on its own as a text miner or a search platform, but in many cases ICA delivers its maximum value together with other products. ICA is a product which helps enriching data by creating structure to unstructured data. The processed data can then be used by other products which normally work with structured data.

Phonetic Algorithm: Bryan, Brian, Briane, Bryne, or … what was his name again?

Let the spelling loose …

What do Callie and Kelly have in common (except for the double ‘l’ in the middle)? What about “no” and “know”, or “Ceasar’s” and “scissors” and what about “message” and “massage”? You definitely got it – Callie and Kelly, “no” and “know”, “Ceasar’s” and “scissors” sound alike, but are spelled quite differently. “message” and “massage” on the other hand differ by only one vowel (“a” vs “e”) but their pronunciation is not at all the same.

It’s a well known fact for many languages that ortography does not determine the pronunciation of words. English is a classic example. George Bernard Shaw was the attributed author of “ghoti” as an alternative spelling of “fish”. And while phonology often reflects the current state of the development of the language, orthography may often lag centuries behind. And while English is notorious for that phenomenon it is not the only one. Swedish, French, Portuguese, among others, all have their ortography/pronunciation discrepancies.

Phonetic Algorithms

So how do we represent things that sound similar but are spelled different? It’s not trivial but for most cases it is not impossible either. Soundex is probably the first algorithm to tackle this problem. It is an example of the so called phonetic algorithms which attempt to solve the problem of giving the same encoding to strings which are pronounced in a similar fashion. Soundex was designed for English only but has its limits. DoubleMetaphone (DM) is one of the possible replacements and relatively successful. Designed by Lawrence Philips in the beginning of 1990s it not only deals with native English names but also takes proper care of foreign names so omnipresent in the language. And what is more – it can output two possible encodings for a given name, hence the “Double” in the naming of the algorithm, – an anglicised and a native (be that Slavic, Germanic, Greek, Spanish, etc.) version.

By relying on DM one can encode all the four names in the title of this post as “PRN”. The name George will get two encodings – JRJ and KRK, the second version reflecting a possible German pronunciation of the name. And a name with Polish origin, like Adamowicz, would also get two encodings – ATMTS and ATMFX, depending on whether you pronounce the “cz” as the English “ch” in “church” or “ts” in “hats”.

The original implementation by Lawrence Philips allowed a string to be encoded only with 4 characters. However, in most subsequent
implementations of the algorithm this option is parameterized or just omitted.

Apache Commons Codec has an implementation of the DM among others (Soundex, Metaphone, RefinedSoundex, ColognePhonetic, Coverphone, to
name just a few.) and here is a tiny example with it:

import org.apache.commons.codec.language.DoubleMetaphone;

public class DM {

public static void main(String[] args) {

String s = "Adamowicz";

DoubleMetaphone dm = new DoubleMetaphone();

// Default encoding length is 4!

// Let's make it 10

dm.setMaxCodeLen(10);

System.out.println("Alternative 1: " + dm.doubleMetaphone(s) +

// Remember, DM can output 2 possible encodings:

"nAlternative 2: " + dm.doubleMetaphone(s, true));

}
}

The above code will print out:

Alternative 1: ATMTS

Alternative 2: ATMFX

It is also relatively straightforward to do phonetic search with Solr. You just need to ensure that you add the phonetic analysis to a field which contains names in your schema.xml:

Enhancements

While DM does perform quite well, at first sight, it has its limitations. We should know that it still originated from the English language and although it aims to tackle a variety of non-native borrowings most of the rules are English-centric. Suppose you work on any of the Scandinavian languages (Swedish, Danish, Norwegian, Icelandic) and one of the names you want to encode is “Örjan”. However, “Orjan” and “Örjan” get different encodings – ARJN vs RJN. Why is that? One look under the hood (the implementation in DoubleMetaphone.java) will give you the answer:

private static final String VOWELS = "AEIOUY";

So the Scandinavian vowels “ö”, “ä”, “å”, “ø” and “æ” are not present. If we just add these then compile and use the new version of the DM implementation we get the desired output – ARJN for both “Örjan” and “Orjan”.

Finally, if you don’t want to use DM or maybe it is really not suitable for your task, you still may use the same principles and create your own encoder by relying on regular expressions for example. Suppose you have a list of bogus product names which are just (mis)spelling variations of some well known names and you want to search for the original name but get back all ludicrous variants. Here is one albeit very naïve way to do it. Given the following names:

CupHoulder

CappHolder

KeepHolder

MacKleena

MackCliiner

MacqQleanAR

Ma’cKcle’an’ar

and with a bunch of regular expressions you can easily encode them as “cphldR” and “mclnR”.

String[] ar = new String[]{"CupHoulder", "CappHolder", "KeepHolder",
"MacKleena", "MackCliiner", "MacqQleanAR", "Ma'cKcle'an'ar"};

for (String a : ar) {
a = a.toLowerCase();
a = a.replaceAll("[ae]r?$", "R");
a = a.replaceAll("[aeoiuy']", "");
a = a.replaceAll("pp+", "p");
a = a.replaceAll("q|k", "c");
a = a.replaceAll("cc+", "c");
System.out.println(a);
}

You can now easily find all the ludicrous spellings of “CupHolder” och “MacCleaner”.

I hope this blogpost gave you some ideas of how you can use phonetic algorithms and their principles in order to better discover names and entities that sound alike but are spelled unlike. At Findwise we have done a number of enhancements to DM in order to make it work better with Swedish, Danish and Norwegian.

References

You can learn more about Double Metaphone from the following article by the creator of the algorithm:
http://drdobbs.com/cpp/184401251?pgno=2

A German phonetic algorithm is the Kölner Phonetik:
http://de.wikipedia.org/wiki/Kölner_Phonetik

And SfinxBis is a phonetic algorithm based on Soundex and is Swedish specific:
http://www.swami.se/projekt/sfinxbis.68.html