# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 2 Conceptual Framework
### Sentiment Analysis  
Sentiment Analysis (SA) infer the sentiment conveyed by a piece of text by relying on (external) lexical resources, which map each term to a categorical (positive, negative, neutral) sentiment score. As an example, terms such as wonderful, beautiful, and joy have a positive sentiment score, while terms such as fear and sadness have a negative one [90]. SA techniques are applied to almost every social domain because opinions are critical to almost all human behaviors [136]. Opinion mining and sentiment analysis methods can be applied to the SM comments [137] to automatically identify issues that concern citizens, as well as features they liked [138]. The task is rather unwieldy because each word has to be treated separately [139]. The fuzzy nature of human emotion provides data that contain a vast amount of uncertainty. Fuzzy systems are capable of addressing this problem while achieving a suitable tradeoff between accuracy and performance [109]. The words that someone uses are not the only source of information emotion recognition. Emotion recognition is closely related to Facial Expression Recognition (FER) [95]. FER-based systems comprise three steps. In the first step, the face is detected in a video stream and is cropped as region-of-interest for the next step. Then, it is resized into specified dimensions so that all the images given to the model become of the same size. In the next step, some low-level or high-level or both features are extracted from the cropped region. In the last step, the features are classified using a classifier.  
User Interface and Visualization  
As [PERSON] et al. argue, IoT applications will only be successful if it is usable by everybody [65]. The operators and dwellers of smart cities are not data scientists, so they have difficulty when applying ML techniques to their applications without experts [140]. Data analysis would be considerably simplified if one could visualize data graphically. Information visualization uses graphic techniques to help people understand and analyze data. Visual representations and interaction techniques take advantage of the human eyes' broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once [75]. The objective of the analysis and visualization of data is to highlight useful information and support decision making with the lowest degree of human intervention [140]. A frontend component should provide users with comprehensive charts that visualize the information [9]. Therefore, interfaces should enable users to intuitively understand the behavior of the system. The interfaces should be easy to use, responsive, mobile, and abstract the complexity of the underlying processes. Once the data from the sensors has been retrieved, it may be helpful for the city operator to have tools available to create dashboards that show this data simply and intuitively [141]. Users should be able to interact with the smart city application with a smart city dashboard that represents the structure, real-time data, and key indexes on a city map [92]. The goal of the dashboard is to provide the user with a set of tools to visualize and handle the aggregated analysis results [90]. Thanks to quickly visualized data tools, identify correlations, and conceive of innovative, unanticipated uses for existing information became easier [38]. Additionally, the integration of speech interfaces shows great potential to interact in natural language with the operators. In addition, augmented reality interfaces are paving new ways to seamlessly access sensors and systems data [65].