Since the early 2000s several European countries have introduced language and citizenship tests as new requirements for access to long-term residence or naturalization. The content of citizenship tests has been often presented as exclusionary in nature, in particular as it is based on the idea that access to citizenship has to be 'deserved'. In this paper, we aim to explore the citizenship tests 'from below', through the focus on the experience of migrants who prepare and take the 'Life in the UK' test, and with particular reference to how they relate to the idea of 'deservingness'. Through a set of in-depth interviews with migrants in two different cities (Leicester and London), we show that many of them use narratives in which they distinguish between the 'deserving citizens' and the 'undeserving Others' when they reflect upon their experience of becoming citizens. In so doing, they negotiate new hierarchies of inclusion into and exclusion from citizenship, which reflect broader neo-liberal and ethos-based conceptions of citizenship.

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There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.

Citizenship tests are arguably intended as moments of hailing, or interpellation, through which norms are internalized and citizensubjects produced. We analyse the multiple political subjects revealed through migrants' narratives of the citizenship test process, drawing on 158 interviews with migrants in Leicester and London who are at different stages in the UK citizenship test process. In dialogue with three counter-figures in the critical naturalization literature-the 'neoliberal citizen'; the 'anxious citizen'; and the 'heroic citizen'-we propose the figure of the 'citizen-negotiator' , a socially situated actor who attempts to assert control over their life as they navigate the test process and state power. Through the focus on negotiation, we see migrants navigating a process of differentiation founded on preexisting inequalities rather than a journey toward transformation.