## 1 Introduction  
The world population is growing at an extraordinary rate and cities are becoming large and dense [1]. Governments are therefore facing challenges in managing such cities and alleviating citizens' problems in a timely and efficient way. Information and communication technologies (ICT) can be used to overcome these challenges by making cities smart. The main objective of smart cities is to make them more attractive, conducive and connected, and also to enhance the living standards of their residents. Research in this area has gained a lot of attention in recent years [2; 3], and as a result of this, citizens feel more connected with the rest of the world. An unprecedented amount of textual data is being produced on social media; this is also known as \"Big Data\". People compose statuses and messages on micro-blogging (e.g., Twitter) and social networking sites to express their opinions on numerous topics. Users of micro-blogging sites can be anyone, including politicians, celebrities, businessmen and students; thus, it is possible to collect information from different communal and age groups in a society. User generated textual data contains information that can improve the decision-making process of governments to improve the lifestyles of their citizens [4; 5]. Sentiment analysis plays a vital role in understanding public demands and uses the information provided in textual data. Recent times have proven that governments may be critically affected if they ignore citizen's demands (or sentiments) or fail to respond appropriately to them [6]. Sentiment analysis or opinion mining is a computational method for automatically detecting the attitude, emotion and sentiment of a speaker in a given piece of text [7]. The simplest form of sentiment analysis classifies reviews, paragraphs or any text as positive or negative [8; 9]. This type of analysis is incapable of handling conflicting sentiments within a text. For example, a simple sentiment analysis approach for the sentence \"Despite having experienced leadership, Government is unable to resolve water crises.\" would annotate it with the label, \"conflicting\". This is due to the concurrent negative and positive sentiments about the government although careful examination would reveal that this sentence is expressing a positive sentiment for the government from the perspective of its leadership and a negative sentiment about the issue of the water crises. Such examples prove that simple sentiment analysis does not provide in-depth information about sentiments and a detailed sentiment analysis is required to capture the multiple dimensions of the opinionated text content [10]. In 2004, a framework was proposed to extract feature-based (or aspect-based) summaries from customer reviews [11]. It works by decomposing conventional sentiment analysis into three subtasks: (1) product features extraction (i.e. identification of the features about which customers have expressed their opinions); (2) assigning sentiments to product features; and (3) generating summaries on the basis of extracted information. This type of feature-based method for sentiment analysis is known as ABSA (aspect-based sentiment analysis). Aspect category detection (ACD) is one of the important tasks in ABSA, which identifies the aspect categories from customer reviews. These categories are often predefined, which makes it a multi-label classification task. For example, in SemEval-2015 [12] and SemEval-2016 [13], categories are assigned from a predefined set of Entity (e.g., restaurant, food, laptop) and Attribute (e.g., design, quality, price) pairs (E#A). In the sentence, \"The biryani is delicious but expensive\", \"food#prices and food#quality\" should be detected as the aspect categories. An opinion without knowing its target is of limited use [14]. Obtained aspect categories are associated with their sentiment polarities to generate opinionated aspect-based summaries as shown in Table 1.