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“Show me the meaning of being lonely”

“Show me the meaning of being lonely”

by Rhys Sandow

At the turn of the millennium Backstreet Boys released their hit single “Show me the meaning of being lonely”. The lyrics talk about realities of heartbreak and the need for connection and understanding associated with losing a loved one. 25 years later the concept of loneliness brings a far more complex meaning and disturbing statistics. Across the globe loneliness is rapidly becoming one of the most urgent public health risks (see the new report from the World Health Organization, From Loneliness to Social Connection: Charting the Path to Healthier Societies). Nearly half of adults in the UK feel lonely occasionally, sometimes, often, or always (see here). Some groups are especially at risk of loneliness, particularly young, disabled people (see here).  

In order to proactively reduce loneliness, we need a greater understanding of  people’s experiences and feelings they describe under the concept of LONELINESS and how these are talked about (see here). This task was explored by Justyna Robinson (Concepts Analytics Lab) and Faith Matcham (Psychology) at the University of Sussex , whose work towards creating a personalised chatbot for loneliness intervention was funded by Sussex Digital Humanities Lab. Ultimately, the headline findings are that

  • Loneliness is generally experienced with similar intensity across demographic groups.
  • But the risk factors differ greatly, particularly according to age.
    • Social media is discussed as a catalyst for loneliness for younger people.
    • Outright social isolation is framed as a more pressing issue for older people.

For this project we explored data on loneliness collected by Mass Observation Project (MOP). In 2019 MOP issued a survey, the directive on Loneliness and Belonging (see here). The directive consists of two parts. Firstly, participants were asked to provide five words that they associate with loneliness, e.g. despair, fear, frustration, quiet, and sad (see Figure 1). Secondly, they provided long-form narrative responses to a series of questions related to the broader topic of loneliness.

The full analysis of this data is presented in our working paper here.

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How language reveals different faces of loneliness?

How language reveals different faces of loneliness

by Justyna A. Robinson, Gayathri Sooraj, and Caitlin Hogan

The issue of loneliness has been discussed in public platforms more frequently in recent years. Just within the past week newspapers have talked about student loneliness, male loneliness, loneliness epidemic. A recently published WHO Report on Social isolation shows that 1 in 6 people globally suffers from loneliness.  The data on loneliness in the UK shows that nearly half of adults feel lonely occasionally, sometimes, often, or always (see here). Some groups are especially at risk of loneliness, such as, young, disabled people (see here). However, the available reports rarely dive into exploring  how people themselves  describe loneliness or what language choice people make when they talk about  loneliness (but see here). These questions are explored by Justyna Robinson (Concepts Analytics Lab) and Faith Matcham (Psychology) at the University of Sussex , whose work towards creating a personalised chatbot for loneliness intervention was funded by Sussex Digital Humanities Lab. Ultimately, our headline findings are that  

  • Loneliness is generally experienced with similar intensity across demographic groups.
  • But the risk factors differ greatly, particularly according to age.
    • Social media is discussed as a catalyst for loneliness for younger people.
    • Outright social isolation is framed as a more pressing issue for older people.  

For this project we explored data on loneliness collected by Mass Observation Project (MOP). In 2019 MOP issued a survey, the directive on Loneliness and Belonging (see here). The directive consisted of two parts. Firstly, participants were asked to provide five words that they associate with loneliness, e.g. despair, fear, frustration, quiet, and sad (see Figure 1). Secondly, they provided long-form narrative responses to a series of questions related to the broader topic of loneliness.  

Loneliness in five words

70 Writers shared the five words they associate with loneliness. In total, 417 individual words were submitted (sentence responses excluded) with 206 different wordsThe top responses included terms such as isolation, sadness, alone. All 206  words expressed various aspects of affective meaning which we measured by adopting the NRC Valence, Arousal, and Dominance (NRC-VAD) Lexicon classification (Mohammed 2025). Each word is assigned a numerical value, from 0 to 1, for each of the three dimensions depending on how strongly they represent a given dimension.

  • Valence refers to the positive (higher score) or negative (lower score) feelings associated with each word.
  • Arousal relates to the intensity of the emotion, with higher scores reflecting greater intensity.
  • Dominance refers to the degree of control exerted by a stimulus, with higher scores corresponding to greater control. To exemplify this, Figure 1 presents the words provided by a younger female of low socioeconomic status, alongside their scores across the three dimensions.
Figure 1: The valence, arousal, and dominance scores for five words provided by a younger female of lower socioeconomic status

Findings highlight remarkable consistency across demographic groups in their conceptualisation of loneliness across the dimensions of valence, arousal, and dominance. Figure 2 highlights that the average NRC-VAD scores for male and female respondents are almost identical.  

A scatter diagram for the valence, arousal, and dominance scores for male and female respondents
Figure 2: A scatter diagram for the valence, arousal, and dominance scores for male and female respondents

Indeed, statistical analysis identified very few meaningful differences in the data set. This speaks to a remarkable homogeneity in the conceptualisation of loneliness measure by the NRC-VAD scores. The one exception to this was that on the dominance dimension there was an interaction between age and socioeconomic status. While age exhibited minimal differences among those in the lower status socioeconomic group, there was a much greater difference in the higher status group (see Figure 3). We consider age as a binary category for the purposes of this analysis, distinguishing between those who qualified for the state pension at the time of data collection (the state pension age was 65 in 2019) and those who did not.  

Figure 3: The interaction between age and socioeconomic status for the dominance scores

Figure 3 shows that the effect of age on the experience of loneliness is much greater among higher status respondents, with those who are older in this category conceptualising loneliness as something that exerts a greater degree of control (0.34), relative to their younger counterparts (0.19). In particular, there is a large difference between status among younger respondents, with those of lower status (0.28) conceptualising loneliness as exerting greater dominance than their higher status counterparts (0.19). 

Loneliness in long-form narratives

The longer, narrative responses to the directive discussed experiences of loneliness both on a personal level and at a broader societal level as well as speculation as to the causes of loneliness as well as potential solutions. In order to understand the ‘aboutness’ of the data we look at key terms.  

The outputs of modified ‘keyword’ analysis (see our blog about modified keyword analysis here) are presented in Table 1 and the key multi-word terms in Table 2 with the responses to the directive compared against the Ententen 2020 corpus as a baseline (see here). 

The top 25 keywords in the responses to the Loneliness & Belonging directive
Table 1: The top 25 keywords in the responses to the Loneliness & Belonging directive
The top 25 multi-word terms in the responses to the Loneliness & Belonging directive
Table 2: The top 25 multi-word terms in the responses to the Loneliness & Belonging directive

While a full analysis of these results is far beyond the scope of the blog here we consider one case-study, that of age.  

Many of the key terms in Table 2 relate to the theme of age, e.g. young people, old age, old people, and elderly people. We have seen from the quantitative analysis of the affective meaning of the valence, arousal, and dominance that there was very little difference between age groups in the conceptualisation of loneliness (save for the interaction between socioeconomic status and age for the dominance scores). However, the longer-form responses highlight something else. 

While both younger and older people are mentioned in the contexts of loneliness, the risk factors associated with these groups differ. Indeed, the discourses in which mentions of these groups appear highlight that the challenges relating to loneliness are not uniform. For example, social media was often mentioned in the context of young people: 

  • In the news it is reported that loneliness is on the increase, particularly amongst young people. This surprises me as young people are constantly communicating with others on social media. However, perhaps seeing what looks like a perfect life on someone’s Facebook account serves to exacerbate feelings of dissatisfaction and loneliness in people whose lives are not running smoothly.  
  • I understand that social media can cause young people to feel lonely and that other people are living a more interesting life. Unfortunately, it is full of lies but people don’t really understand that.

In contrast, discussions of older people’s loneliness were often framed in terms of isolation in more absolute terms, e.g.: 

  • I know that there are old people today who feel lonely. Particularly following the death of their spouse.  
  • Inevitably, as people live longer and stay in their own homes, isolation can become a serious problem. I believe the older generation can suffer considerably in this respect, particularly in rural communities where the shop and pub are now closed, the bus service has been withdrawn and children have grown up and live elsewhere. 

Ultimately, using a suite of analytical tools, from sentiment analysis, to keyword analysis, to discourse analysis, enables one to garner insight into the conceptualisation of loneliness that is often missed in survey-type data. The discursive nature of this data enables lived-experiences of loneliness and its perceived risk factors to be unpacked in detail- the sort of detail that computational linguistic methods can cut through, to pick out key patterns and affective meanings. Ultimately, the findings from this report will inform the development of a chatbot that will serve as a tool to combat the loneliness crisis.  

If you are interested in working with Concept Analytics Lab, please do contact us at Justyna.Robinson@sussex.ac.uk

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We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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Effective Date: 28.05.2025

Concept Analytics Lab is committed to ensuring digital accessibility for people with disabilities. We are continually improving the user experience for everyone and applying the relevant accessibility standards in line with our academic values and obligations.

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This statement was prepared on 16.05.2025 and last reviewed on 28.05.2025.The accessibility of this website was evaluated using a combination of manual and automated tests.

 

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Effective Date: 16.05.2025
This Privacy Policy explains how Concept Analytics Lab (“we”, “us”, or “our”) collects, uses, and protects any personal data obtained through our website at https://conceptanalytics.org.uk/. We are an academic research initiative affiliated with the Sussex Digital Humanities Lab at the University of Sussex. As such, our data practices are aligned with the University of Sussex’s policies and responsibilities as a registered data controller under the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018.

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Concept Analytics Lab operates within the Sussex Digital Humanities Lab, based at the University of Sussex. The University is the registered data controller (ICO registration number: Z6428144). For questions or concerns please contact:

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We do not collect personal data through this website unless you choose to contact us directly (e.g. via email or form submissions). In such cases, your data will be processed in line with the University of Sussex’s policies.

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We do not use your data for marketing purposes or share it with third parties unless explicitly required or permitted by law.

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As part of our academic mission, we may collect and process personal data for research purposes. This includes, but is not limited to, data collected through:

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We do not use cookies for advertising or personalised tracking. However, our website uses Google Analytics, to collect anonymised information about how visitors use the site. This helps us understand user behaviour, such as which pages are most frequently viewed and how users navigate the site, so we can improve functionality and content.

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These cookies do not identify you personally. We do not combine this data with any other personal information.

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We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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The Labour Party’s 2024 Manifesto – The Voice of Members or Lobbyists?

The Labour Party’s 2024 Manifesto - The Voice of Members or Lobbyists?

by Justyna A. Robinson and Rhys Sandow

CAL Briefing Paper 1

published online on 3rd July 2024

The Labour party has been criticised recently for the volume of its candidates in the 2024 general election with backgrounds in lobbying [1,2,3]. Indeed, this led Novara Media (2024) to question whether the Labour Party is ‘The Lobbyists’ Party’ [4, also 5]. In this article, we assess this claim by considering extent to which the Labour Party’s 2024 general election manifesto is consistent with either the desires of party members or of lobby groups, as articulated in the Labour party’s 2023 National Policy Forum Consultation on progressive trade policy [6]. 

While Labour’s 2023 National Policy Forum (hereafter, NPF) considered a variety of policy areas, our focus here is its forum on trade. Excluding duplicates, there are 302 submissions to the consultation, with 109 submitted by guests (i.e. business and other lobby interest groups), 187 by Labour Party members and the rest by National Policy Forum representatives. In total, there are 244,894 words submitted to the forum. While the majority of submitters were Labour Party members, they produce a minority of the total word count, with 24.7% of words produced by members, as opposed to 71.2% by guests (the rest by NPF representatives). For a more detailed take in the data see Gasiorek et al (2024) [7].

We contrast the top multi-word terms (i.e. phrases) that are used disproportionately by Labour Party guests versus and Labour Party members (see Table 1), and vice versa (Table 2) [8]. Note that the results are ordered by ARF (Average Reduced Frequency) which is a modified frequency measure that accounts for distribution across the submissions, so that one response does not skew the results, see Sandow & Robinson (2024) [9]).  

Table 1 shows that relative to members, guests are more concerned with the mechanisms and process of trade, as highlighted by phrases such as supply chain, due diligence, value chain, and new law. The guests are also more concerned with environmental issues, such as environment act and environmental harm. Additionally, guests focus on human rights, which is a theme represented by phrases such as human rights defender, forced labour, modern slavery, and gender equality.

So, does the Labour Party Manifesto address these considerations?

The Labour Party Manifesto [9] identifies the need for resilient supply chains (resilient supply chains is in the wording of one of the questions in the NPF), as expressed with the following:

“We will ensure a strong defence sector and resilient supply chains, including steel, across the whole of the UK.” (2024 Labour Manifesto)

The environment is a key theme in the Manifesto, with energy and climate occurring 68 and 24 times, respectively. In particular, the ways in which trade agreements can be a vehicle for pursuing a green agenda is recognised in the Manifesto in the following way:

“We will seek a new strategic partnership with India, including a free trade agreement, as well as deepening co-operation in areas like security, education, technology and climate change.” (2024 Labour Manifesto)

Human rights, including gender equality, are also mentioned throughout the Manifesto, although not discussed directly in the context of trade.

We will use the UK’s unique position in NATO, the UN, G7, G20, and the Commonwealth to address the threats we face, and to uphold human rights and international law.” (2024 Labour Manifesto)

 

“Labour will take action to reduce the gender pay gap, building on the legacy of Barbara Castle’s Equal Pay Act.” (2024 Labour Manifesto)

The Manifesto does not mention due diligence, although it is reasonable to assume that this is implicit under the umbrella of good practice and does not mention any new laws in relation to trade, although they do commit to other new laws, e.g. ‘Martyn’s Law to strengthen security of public events.

 

Now, turning the attention to the submissions by the Labour party members. Table 2 shows the phrases that are used disproportionately by Labour Party members, relative to guests.

 

Note, due to the lower quantities of text produced by Party members, here we present the top ten phrases (Table 2), as opposed to the top twenty phrases for the Party guests presented in Table 1.

Party members are more likely to discuss arms which is evident via their use of phrases such as, arms export, arms trade, counter proliferation, including, but not limited to, the Israeli government. They are also more likely to discuss local government and asylum seekers. In context, these phrases typically reference a desire for greater regulation and moral consideration on those with whom we trade arms, greater power for local governments, and a more welcoming environment for asylum seekers.

In terms of the theme of arms, the Manifesto makes a commitment to upholding international law, but avoids references any specific nation or any specific commitments beyond that:

Labour will support industry to benefit from export opportunities in line with a robust arms export regime committed to upholding international law.” (2024 Labour manifesto)

While the Manifesto does not mention Israel/Palestine in relation to arms exports, they do address this elsewhere:

Long-term peace and security in the Middle East will be an immediate focus. Labour will continue to pish for an immediate ceasefire, the release of all hostages, the upholding of international law, and a rapid increase of aid into Gaza. Palestinian statehood is the inalienable right of the Palestinian people. It is not in the gift of any neighbour and is also essential to the long-term security of Israel. We are committed to recognising a Palestinian state as a contribution to a renewed peace process which results in a two-state solution with a safe and secure Israel alongside a viable and sovereign Palestinian state.” (2024 Labour Manifesto)

There are multiple commitments in the Manifesto to provide greater autonomy for local and devolved governments, such as:

“Local government is facing acute financial challenges because of the Conservatives’ economic mismanagement which sent interest rates soaring, along with their failures on public services. To provide greater stability, a Labour government will give council multi-year funding settlements and end wasteful competitive bidding.”  (2024 Labour Manifesto)

Lastly, while both the Party members’ responses to the NPF and the Manifesto discuss asylum seekers, the framing of asylum seekers differs in each context. In the NPF, members implore the Labour Party to provide greater support for asylum seekers:

We must promote Labour values by treating refugees and asylum seekers with dignity and respect. This means providing a safe, legal route for refugees and ending detention in camps while claims are being processed. Asylum claims must be processed more quickly and people should be permitted to work while their claims are being processed.” (response to the 2023 Labour National Policy Forum Consultation on progressive trade)

Whereas, in the Manifesto, asylum seekers are discussed in the context of perceived Conservative failures:

Rather than a serious plan to confront the crisis, the Conservatives have offered nothing but desperate gimmicks. Their flagship policy- to fly a tiny number of asylum seekers to Rwanda- has already cost hundreds of millions of pounds. Even if it got off the ground, this scheme can only address fewer than one per cent of the asylum seekers arriving.” (2024 Labour Manifesto)

Or in the context of returning failed asylum seekers to safe countries:

“We will negotiate returns arrangements to speed up returns and increase the number of safe countries that failed asylum seekers can swiftly be sent back to.” (2024 Labour Manifesto)

To conclude, the Labour Party Manifesto speaks to both the concerns of both lobbyists and Party members, as well as where they intersect, for example by aligning more closely with the EU.  However, the concerns highlighted in the forum are not always addressed directly in relation to trade, but in broader policy commitments. Also, the ways in which these topics were addressed were not always consistent between the Forum and Manifesto. For example, while NPF responses advocated greater dignity and respect for asylum seekers, this was not explicit in the Manifesto. Ultimately, by and large, the Manifesto does not demonstrate a bias towards lobbyists, but manages to find an equilibrium between the desires of two distinct, yet critically important, groups.

References

[1] https://novaramedia.com/2024/06/13/meet-the-labour-candidates-lobbying-for-oil-gas-and-arms-companies/ . Accessed 3rd July 2024.

[2] Concern over ‘corrosive’ impact of Labour candidate working as lobbyist. The National. https://www.thenational.scot/news/24205441.concern-corrosive-impact-labour-candidate-working-lobbyis/. Accessed 3rd July 2024.

[3] Labour’s corporate lobbying links with Polly Smythe. Macrodose Election Economics. https://open.spotify.com/episode/6UmKWzzXx8XwU1Q8iPKH3A . Accessed 3rd July 2024.

[4] Novara Media @novaramedia. (2024). (video). Tik Tok. https://www.tiktok.com/@novaramedia/video/7382148715156376865?lang=en . Accessed 3rd July 2024.

[5] The Labour Party becomes the Lobbyists Party. Morning Star. The Labour Party becomes the Lobbyists Party | Morning Star (morningstaronline.co.uk) . Accessed 3rd July 2024.

[6] National Policy Forum Consultation (2023) Available via  https://policyforum.labour.org.uk/commissions. Accessed 13th June 2024.

[7] Gasiorek, Michael, Justyna A. Robinson, and Rhys Sandow (2024) Labour’s Progressive Trade Policy: Consultations and policy formulation. UKTPO Briefing Paper 81 – June 2024. Available via https://blogs.sussex.ac.uk/uktpo/publications/labours-progressive-trade-policy-consultations-and-policy-formulation/ Accessed 3rd July 2024.

[8] The analysis is done via SketchEngine. Available via http://www.sketchengine.eu/

[9] Sandow, Rhys and Justyna A. Robinson (2024) How can you identify key content from surveys?  Concept Analytics Lab Blog. Available via https://conceptanalytics.org.uk/identifying-key-content-from-surveys/ Accessed 13th June 2024.

[10] Labour Party Manifesto. (2024). Available via https://labour.org.uk/wp-content/uploads/2024/06/Labour-Party-manifesto-2024.pdf. Accessed 13th June 2024.

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We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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How does concept-led research matter?

How does concept-led research matter? 

How does concept-led research matter?

by Caitlin Hogan

 

Our Concept Analytics Lab (CAL) team LOVES concepts. In our daily work, we keep seeing the value of the concept-based view of language in bringing insight to thinking, attitudes, and behaviours of people. But how important is the concept-based research for a wider linguistic community? Can concept-based research impact other disciplines and industries? Can you commercialise your concept-based knowledge?

With the aim of consolidating research and application of concept-based approaches to text analysis we gathered experts in the field for the first Concept Quest conference.

 

The event Concept Quest: Navigating Ideas on and Through Linguistic Concepts took place in March 20204 at the University of Sussex. It focussed on the work of CAL and other researchers from a range of academic disciplines. We hosted talks and panels from scholars studying everything from AI concepts to the impact of trade deals on the economy and commercialising concepts in the process of wine production.

 

Justyna Robinson, the Director of the Concept Analytics Lab, started by talking about the aims and advantages of concept mining as a methodology. Concepts are not encapsulated by a single word but are be observable by a set of words, phrases and/or constructions. This allows us to understand how individual terms might be used differently over time, and how they may come to represent different concepts. CAL’s researcher Rhys Sandow then discussed how one can visualise conceptual ontologies and showed how one can turn complex sets of lexical relations into clear diagrammatic representations. Such representations can shed light on conceptual, including socio-conceptual, differences that are inaccessible to more traditional approaches to the analysis of large texts.

Following this, Louise Sylvester (Westminster) talked about how concepts can be incorporated into studies of Medieval English. Her work focuses on the adoption of terms from French into English during this period, and through the use of a semantic hierarchy, she is able to inspect in which cases French pushed out the English variant, and in which cases this did not occur. The use of concepts allows us to see the patterns that emerge in synonym relationships, even from long ago.

 

Haim Dubossarsky (QMUL) approached the study of concepts from a computational angle, discussing the ways in which we currently carry out computational and corpus linguistics, such as collocations, and how we can improve on these methods. Through the projection of a word’s usage onto a series of vectors, one is able to map the meanings of the word and their change over time. This technique provides a computational boost to the analysis of meaning and represents an important link between the world of linguistics and that of computer science that the Concept Analytics Lab covets.

 

The talks on theoretical and methodological aspects of doing concept research were complemented by talks addressing applications of concepts in archival work and in commercial endeavours. 

 

Piotr Nagórka (Warsaw’s Cultural Terminology Lab) discussed the exploration of communications systems and terminological sciences. He probed how the terminology we use to refer to types of wine maps onto production process itself. In this case, for wine. His work shows how one might commercialise concept research by marrying the study of concepts with processes and techniques within the manufacturing sciences.

Angela Bachini and Kirsty Patrick, who work on the Mass Observation project helped us understand how archivists arrive at identifying important concepts in indexing of a new text. We learned a great deal from the Mass Observation team about their workflow and how we as researchers can best help archivist to automate indexing via key-concept detection.

The event finished with a panel discussion on why concepts matter led by Lynne Murphy (Sussex), in which Piotr Nagorka, Kirsty Pattrick, were joined by Julie Weeds (Sussex AI) and Alan Winters (Sussex, CITP).  Alan reflected on the value of concepts in trade analysis, particularly to understand the trade-offs that people are willing to make with regard to global trade. These kind of complex attitudes are difficult to access with other methods, particularly the quantitative methods often used in economics. The advantage of concept analysis, where participants can describe their accounts in rich detail which can then be computationally analysed, is clear in this case. Louise Sylvester added that in her work on Medieval English, concepts help us understand how people living in that era made sense of the world and what categories were meaningful for them. This helps greatly with noticing patterns of use in historical linguistics, and also helps us to understand how the concept of something like a farm has changed from the middle ages to the present day.

 

We continued chatting over some delicious wine (thanks to a generous sponsorship from Mass Observation) and made new connections across institutions and fields.  This is exactly the kind of result we envisage from a successful colloquium, and we were proud to have hosted such a stimulating day. Our gratitude extends to all the wonderful speakers and attendees for making this event so brilliant!

 

To conclude our reflections, the Concept Quest highlighted the value of concept-based and concept-led research and applications. Researching concepts matters for theory of language and knowledge representation as we consider conceptual hierarchies, lexicalised and non-lexicalised concepts, and emergence of new concepts/ideas. At a methodological level, concepts pose a challenge for traditional word-based corpus and NLP techniques. Therefore, new ways of extracting conceptual information from big data is needed.  At a more applied level, empirical ways of gaining access to conceptual information are invaluable for other sectors and disciplines which use large text data. Thus, strengthening objectivity and replicability of concept research will open up this research for other sectors which seek more expert analyses.  That development can also lead to impactful research and even commercialisation of conceptual research.

 

Please get in touch here to find out which key concepts and themes are revealed in your data. 

 

References

Robinson, J. A., Sandow, R. J., & Piazza, R. (2023). Introducing the keyconcept approach to the analysis of language: the case of regulation in COVID-19 diaries. Frontiers in artificial intelligence, 6.

Nagórka, P. (2021). Madeira, Port, Sherry. The Equinox Companion to Fortified Wines. Equinox Publishing Limited.

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We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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Identifying key content from surveys

How can you identify key content from surveys?

by Rhys Sandow and Justyna Robinson

 

A case of responses to the Labour Party’s 2023 Trade Policy Forum

 

Surveys which collect responses to open questions are a popular and valuable way of gauging peoples’ attitudes. But they also present specific challenges for keyness analysis in corpus linguistics as the results can be misleading. For example, a high frequency of term X may be skewed by one or two documents within the corpus, rather than being representative of attitudes among the survey respondents more broadly. In such cases, traditional corpus linguistic measures of difference, such as relative frequencies or keyness are not appropriate. In such cases, we advocate for the use of measures of dispersion across a corpus, such as Average Reduced Frequency (ARF) and Document Frequency (DOCF).  This distinction between frequency and dispersion is critical to develop meaningful insights into large data sets, particularly in the context of policy consultation where an understanding of plurality and consensus is highly important.

 

Let us demonstrate how to solve this problem on the basis of examples from data we recently analysed.  Concept Analytics Lab (CAL) was tasked by the UK Trade Policy Observatory (UKTPO) to analyse responses to the Labour Party’s Trade Policy Forum in the build-up to the Labour Party’s annual conference in October 2023. The survey gathered 302 answers to seven questions comprising c. 250,000 words of data. Many of the submissions came from groups with very particular interests, such as specific industries or specific local communities. Therefore, some responses contained detailed discussions of issues critically important to the submitter, but not necessarily widespread among all respondents. For example, when running a keyword analysis, the eighth most key word (with the ententen21 corpus as our baseline) was gpi (genuine progress indicator) with 35 hits across the corpus. However, upon closer inspection, these hits are spread across only 2 of the 302 responses. Thus, while gpi has a high keyness score, it cannot be said that it is a salient topic across the corpus as its use is so highly concentrated across 0.66% of documents.

In order to remedy this limitation of keyness analysis, we considered the spread of terms across the corpus using Sketch Engine’s Average Reduced Frequency (ARF) statistic. ARF is a modified frequency measure that prevents results being skewed by a specific part, or a small number of parts, of a corpus (for more detail on the mathematics behind the measure, see here). Where the ARF and absolute frequency are similar, this suggests a relatively even distribution of a given term across a corpus. However, when there are large discrepancies between the absolute frequency and ARF, this is indicative of a skew towards a small subset of the corpus. For example, while the absolute frequency of gpi in the corpus is 35, the ARF is 2.7 (DOCF, 2), highlighting its lack of dispersion. Similarly, the term gender-just has an absolute frequency of 19 but an ARF of 1.32 (DOCF, 1), highlighting that this term is not characteristic of the data set as a whole, but is highly salient within a small subset of the corpus. By contrast, labour, with an absolute frequency of 1, 434 had an ARF of 725.74 (DOCF, 226), highlighting its spread across the corpus.

When analysing corpus data, methodological decisions can have highly impactful repercussions for the analysis. For example, let’s take the top 10 key multi-word terms from the Labour Party Policy Forum data set ordered by keyness score (see Table 1) and compare it with the top 10 multi-word terms ordered by the highest ARF statistic (see Table 2).

Table 1: The top multi-word terms, ordered by keyness score
 
Table 2: The top multi-word terms, ordered by ARF
 

This analysis highlights, in particular, two obvious outliers, namely ‘human rights defender’ and ‘modern slavery’. The low DOCF and ARF scores highlight that they are highly concentrated within a small number of submissions and, so, are not characteristic of the data set more broadly. 

While no multi-word term occurs in the majority of documents, table 2 provides a perspective on the most broadly dispersed multi-word terms.  It is important to note the substantial overlap between the two measurements in tables 1 and 2, e.g. ‘trade policy’, ‘trade deal’, ‘trade agreement’, ‘international trade’, and ‘labour government’, appear in both. However, the advantage of the ARF ordered data is that there are no clear outliers, skewed by individual, or a very small number of, responses. This means that it is the second data which provides a more valid overview of the content of the data set.

Using a traditional approach to keyness analysis, conclusions may recommend interventions around trade and human rights defenders or modern slavery. However, an analysis of ARF highlights that this is misleading and does not get to the essence of the data set. What is more, policy recommendations based on the former statistic only may result in the disproportionate influence of those who lobby in relation to very specific terms at the expense of more widespread priorities and concerns.

 

This ARF analysis formed part of our analysis of the 2023 Labour Party’s Policy Forum that we conducted for the UKTPO, which can be accessed here.

 

If you are interested in our data analysis services or partnering with us in any way, please contact us here

 

References

Labour Policy Forum (2023). National Policy Forum Consultation 2023. Britain in the World..

Gasiorek, M and Justyna Robinson. (2023) What can be learnt from the Labour Party’s consultation on Trade? UKPTO Blog. 

About Us

We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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Survey of English Usage zooms on concepts

Survey of English Usage zooms on Covid-19 concepts

by Caitlin Hogan

 

Lab leader Dr Justyna Robinson gave a talk at University College London (UCL) as part of the Survey of English Usage Seminar Series about the work of the Concept Analytics Lab. Her talk covered a wide range of issues in the realm of concept analytics, including how to draw out concepts from written accounts via the Mass Observation Archive dataset. She focussed in particular on the role of concept change during the COVID-19 pandemic, when lifestyle changes forced people to adapt their routine, and thus the concepts they mention in their daily accounts to shift, in some cases drastically. 

 

The Mass Observation Archive began in 1937 founded by Tom Harrisson, Charles Madge and Humphrey Jennings, and its original tenure ran until the 1960’s, at which point it became defunct. Originally inspired by the founders’ desire to capture public opinion on the abdication of King Edward VIII, by 1939 the project aimed to have ordinary people record the day-to-day experiences of their lives, and nearly 500 did. This creates an invaluable documentation of peoples’ habits, lives, and thoughts, acting almost as a time capsule. In 1981, it was revived at the University of Sussex and continues to collect qualitative accounts of ordinary peoples’ lives and opinions to this day. Every 12th of May (chosen as it was the anniversary of the coronation of King George VI), the project calls for anyone to submit a record of their activity on that day, in honour of the original 1937 call going out on that same day.  The 12th May diaries collected  during COVID-19 pandemic were digitised by a grant provided by the Wellcome Trust. Digitised diaries from the first lockdown in the UK, i.e. 12th May 2020, were the focus of Justyna’s talk. 

 

Justyna discussed how records of ordinary peoples’ activities during lockdown marked a shift towards concepts such as REGULATION, which may be expected, but also the discussion of furniture, given the struggles we all had to adapt to working from home.  Excerpts from the diaries on this theme include the following examples:

 

  • most of the online activities I could cast from my phone to the TV or could be done on my phone, which was vital during the early stages of lockdown, as XXXX was using the home laptop to work remotely, until he received a laptop through work
  • I’m working from home and the work PC is on an old computer desk so giving me a 2foot space to work in. 
  • I can also stretch and do yoga during my working day and sit at a desk that is the right size for me- I am very petite and used to feel uncomfortable in the chairs in meeting rooms, designed for men. 

 

As these examples show, participants mention the struggles of accommodating working from home with limited resources in terms of space and furniture for use while working, and the struggles coexisting while some household members work, and others use furniture for other purposes. The examples illustrate clearly that we can talk about the same concept without using the exact same words, so this commonality would be lost if we only used simple corpus linguistic techniques in this analysis. As explained in the Robinson et al (2023), terms like restriction, freeze, coordination, and clampdown emerged while talking about regulations in the COVID-19 pandemic but were not exactly the word regulation itself. Linking these lexemes together allows a clearer picture to emerge of what topics participants wrote in their diaries. The insight into which concepts participants found important during lockdown would not have been detectable without concept analysis,  and especially invoking the notion of a keyconcept (Robinson et al, 2023),

 

 

As the lab continues to refine tools for concept analysis, talks such as this one is key to spread the word to new and emerging scholars about the role of concepts when surveying English usage. 

 

References

Robinson J.A., Sandow R.J. and Piazza R. (2023) Introducing the keyconcept approach to the analysis of language: the case of REGULATION in COVID-19 diaries. Front. Artif. Intell. 6:1176283. doi: 10.3389/frai.2023.1176283 

About Us

We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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Concept Quest Event, 11th March 2024​

Concept Quest Event, 11th March 2024

by Caitlin Hogan

Concept Quest: Navigating ideas on and through linguistic concepts

Our lab will be part of an exciting event in collaboration with the University of Sussex Digital Humanities Lab and the Mass Observation project. Our session will cover our work on concept analysis through some of our recent projects. The team is excited to attend and present at such a thought-provoking gathering!

 

Be sure to check back here after the event for another blog post and photos! 

Register for the event here:

https://www.ticketsource.co.uk/shl-events-ticket/t-yamopvl

 

 

About Us

We identify conceptual patterns and change in human thought through a combination of distant text reading and corpus linguistics techniques.

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