## 3 Detection Approaches Review  
In the following subsections, approaches to the detection of the state of the user by analyzing different sources of data and fusion techniques will be reviewed. Performance of the reviewed approaches and techniques used are summarized in Tables 1 and 2, respectively. Datasets used, their characteristics and the partitions used for training and testing are summarized in Tables 3 and 4 (these two later tables will be referred to as the 'dataset tables' in the following text of this paper). From the dataset tables, it can be seen that annotated customer reviews about products in e-commerce websites such as Amazon are very useful to construct datasets for sentiment analysis models. Annotated reviews extracted from various sites not related to e-commerce can also be useful for this purpose, and they can be found from very variated sources (e.g., the Internet Movie Database, epinions.com, CitySearch.com). Moreover, in the dataset tables it is shown that when building a dataset, researchers can make use of data streaming services from SNSs, such as Twitter, or download videos and images from online platforms that allow users to share images and videos (e.g., Youtube, Flickr). Then for labeling the data researchers can make use of labeling services such as the one offered by Amazon (Amazon Mechanical Turk or AMT). If there is no need for very large datasets, controlled laboratory experiments with a set of people to generate a dataset are always an option, or reusing existing datasets, as is shown as well in the dataset tables. Additionally, in the dataset tables can be seen that specific requirements such as people reacting to a specific set of emotions, or collecting data under certain conditions of physical and cognitive stress might require to conduct a laboratory experiment, since those might be hard to find and reuse. Stress-related data can be collected online like in the case of sentiment analysis datasets, using, for example, SNSs data. Firstly, we review sentiment analysis on text data, which is one of the most predominant and well-established lines of work in recent years regarding data analysis for detecting the state of the user. Following we review visual sentiment analysis, a more recent line of work on sentiment analysis that gives a new approach to the problem of detecting sentiment polarity of the users. In the same way, we later review works on another approach to the sentiment analysis task, which is sentiment analysis on audio data. We follow with a review of works on multi-modal fusion sentiment analysis, which refers to works that address sentiment analysis with a combination of techniques, using analysis of text and audio data and other sources of data. Also in these works, different levels of fusion are investigated. Next, we continue reviewing sentiment analysis works that use CBR technologies for their approaches. Finally, we finish with a review of works on stress analysis and works that use keystroke dynamics data to detect sentiment and stress. For covering the works most relevant to the aim of this review, we define an inclusion/exclusion criteria as follows:  
* Is it relevant to the works being reviewed in a given section? (e.g., a work included in the sentiment analysis on audio data subsection has to focus on sentiment analysis techniques applied to audio). * The work uses a technique or techniques to address the problem which are different than the ones used in other works reviewed previously in the same section? (e.g., dictionary-based methods are different from machine learning models for sentiment classification in sentiment analysis). There can be works using the same technique as another reviewed work if the problem addressed by the paper is different (e.g., one work addresses emotion detection using big data, which is different from other work performing emotion detection on stored data or on single users). * Does the work provide experiments and data that gives insight on the usefulness of the used technique to address the problem at its focus?