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.

Graph Search from Down Under

We’ve already written about the new concept called Graph Search, which is being popularized by Facebook. Wouldn’t it be cool if we applied this to the enterprise as well, as I wrote in an earlier blog post on Enterprise Graph Search? That’s what Australian startup company Lumanetix thinks, when they created the SPAR-K graph search engine for the enterprise.

Applied graph search

As seen in the screenshots of the product, the product do queries against relational databases with linked data objects such as Movies linked to People in Casts, or Managers of Departments in an organization. One difference to Facebook graph search is the more Google-like query syntax which is keyword-based where Facebook uses natural language processing to describe specific queries.Graph search applied to the enterprise

It’s exciting to see that the market is picking up speed with new innovations in the enterprise search field, as Lumanetix SPAR-K is an example of.

 

/Christian Ubbesen

SLTC 2012 in retrospect – two cutting-edge components

The 4th Swedish Language Technology Conference (SLTC) was held in Lund on 24-26 October 2012.
It is a biennial event organized by prominent research centres in Sweden.
The conference is, therefore, an excellent venue to exchange ideas with Swedish researchers in the field of Natural Language Processing (NLP), as well as present own research and be updated of the state-of-the-art in most of the areas of Text Analytics (TA).

This year Findwise participated in two tracks – in a workshop and in the main conference.
As the area of Search Analytics (SA) is very important to us, we decided to be proactive and sent an application to organize a workshop on the topic of “Exploratory Query Log Analysis” in connection with the main conference. The application was granted and the workshop was very successful. It gathered researchers who work in the area of SA from very different perspective – from utilizing deep Machine Learning to discover users’ intent,  to looking at query logs as a totally new genre. I will do a follow-up on that in another post. All the contributions to the workshop will also be uploaded on our research page.

As for the main conference, we had two papers accepted for presentation. The first one dealt with the topic of document summarization – both single and multidocument summarization
(http://www.slideshare.net/findwise/extractive-document-summarization-an-unsupervised-approach).
The second paper was about detecting Named Enities in Swedish
(http://www.slideshare.net/findwise/identification-of-entities-in-swedish).

These two papers presented de facto state-of-the-art results for Swedish both when it comes to document summarization and Named Entity Recognition (NER). As for the former task, there is neither a standard corpus for evaluation of summarization systems, nor many previous results and just few other systems which made it unfeasible to compare our own system with. Thus, we have contributed two things to the research in document summarization – a Swedish corpus based on featured Wikipedia articles to be used for evaluation and a system based on unsupervised Machine Learning, which by relying on domain boosting achieves state-of-the-art results for English and Swedish. Our system can be further improved by relying on our enhanced NER and Coreference resolution modules.

As for the NER paper, our Entity recognition system for Swedish achieves 74.0% F-score, which is 4% higher than another study presented simultaneously at SLTC (http://www.ling.su.se/english/nlp/tools/stagger). Both systems were evaluated on the same corpus, which is considered a de facto standard for evaluation of different NLP resources for Swedish. The unlabelled score (i.e. no fine-grained division of classes but just entity vs non-entity) of our system achieved 91.3% F-score (93.1% Precision and 89.6% Recall). When identifying people, the Findwise NER system achieves 78.1% Precision and 90.5% Recall (83.9% F-score).

So, what did we take home from the conference? We were really happy to see that the tools we develop for our customers are not something mediocre but rather something that is of very high quality and is the state-of-the-art in Swedish NLP. We actively share our results and our corpora for research perposes. Findwise showed keen interest in cooperating with other researchers in developing better tools and systems in the area of NLP and Text Analytics. And this I think is a huge bonus to all our current and prospective customers – we actively follow the current trends in the research community and cooperate with researchers, and our products do incorporate the latest findings in the field, which make us leverage both high quality and cutting-edge technology.

As we continuously improve our products, we have also released a Polish NER and some work has been initiated on Danish and Norwegian ones. More NLP components will be soon available for demo and testing on our research page.

Analytics and Big Data at IBM Information On Demand 2011

The big trend these days are in Big Data and how you can analyze large amounts of information in order to gain important insights, and from those insights be able to take the right action. This trend was a hot topic at the IBM Information On Demand (IOD) conference in Las Vegas earlier this year. IBM has a very strong position in this field, it’s hard to have missed how their computer system Watson challenged the top players of all time in Jeopardy recently, and won! Read more about Watson

Now IBM has taken the technology behind Watson and started to apply it in their different analytics products, where one specific area that is being targeted is healthcare. For this area IBM released a new product during IOD called IBM Content and Predictive Analytics for Healthcare, which can for example be used as a tool for physicians to support them in their diagnosis of patients.

In April this year IBM merged two of their products, their search engine OmniFind and their product for analyzing large amounts of unstructured information, Content Analytics. The new product is called IBM Content analytics with Enterprise search and it too is based on much of the same technology that is used in Watson, more specifically it utilizes the same Natural Language Processing techniques. This means that it has the ability to understand text on a level just as sophisticated as that of Watson.

Content Analytics with enterprise search scales very well to many millions of documents. However, when there is a need for analyzing really enormous data sets, in the magnitude of petabytes or even exabytes, IBM has developed what they call their BigData platform. This platform mainly revolves around two products, InfoSphere Streams and InfoSphere BigInsights, and it builds on a foundation of open source software, such as Apache Hadoop and Apache Lucene. InfoSphere Streams is used for real time analysis of information in motion. This helps you understand what’s happening right at this moment in your organization and supports you in taking appropriate action as things are happening. InfoSphere BigInsights on the other hand lets you analyze and draw insight from massive amounts of already existing data.

Studies have shown how organizations that fall short in this area are overtaken by those who understand how to use the power of analytics.

IBM has surely chosen an interesting path when merging Analytics with Findability.