## 3 Detection Approaches Review
### Sentiment Analysis on Text  
Sentiment analysis can be applied to different kinds of media. In this section, we will review state-of-art works in the line of applying sentiment analysis to texts and using distinct techniques. Sentiment analysis on texts has been assessed with four well-differentiated techniques in the literature, which are document-level sentiment analysis, sentence-level sentiment analysis, aspect-based sentiment analysis, and comparative sentiment analysis [31]. Starting from sentiment detected in an entire document, to sentiment in a sentence and finally in an aspect, which is a sequence of words representing an aspect in the text (e.g., the government, love, group of people). In other words, depending on the level of fine-grained analysis that we want to perform we will be choosing one or another. Comparative sentiment analysis does not apply to this concept, it is an exception to the techniques of sentiment analysis on text mentioned in this paragraph, where we use comparative words as the elements of the model with a sentiment polarity associated. The model is trained so we can learn which ones are the preferred entities using comparative sentences [31]. Document-level sentiment analysis has two important issues. Having to give an aggregate value of sentiment for an entire document, and that it may, and potentially will contain a variated set of polarities in different sections of it. For these reasons, researchers developed more fine-grained levels of sentiment analysis. Sentence-level is easier but still could potentially find more than one polarity in the same sentence, which may lead to a conflict when trying to generate an aggregated sentiment for the sentence. Thus, aspect-level sentiment analysis was created, which focuses on concrete aspects or entities and tries to give as output a sentiment polarity associated with them. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Dataset or Datasets** & **Partitions** \\ \hline  & Development corpus: this corpus is a collection of 3000 stress-related tweets, manually classified by the author for stress and relaxation. These tweets were identified by monitoring a set of stress and relaxation keywords over a week; six corpora of English short text messages extracted from Twitter.com (tweets), and coded by humans with stress and relaxation strengths. & For assigning term strengths, identify missing terms, and to refine the sentiment term scores the development corpus was used. The performance of the supervised version of TensiStrength was evaluated using 10-fold cross-validation 30 times on the data of the six English short text datasets, with the average scores across the 30 iterations recorded. \\ \hline  & 24 participants, with ages ranged from 18 to 56, being 14 female, 10 male and 22 right-handed. They were asked to type with a keyboard after cognitive and physical stress tasks. Sessions spread over at least 3 days, ranging from 3 to 22 per participant, with a median of 9 days. The data collected was information about event (key or down), time stamp (10 ms resolution), and key code. After each task, participants self-reported their stress level. & Baseline condition, control condition, and 2 experimental conditions were used. Baseline: 10 samples under no stress. Control: two samples under no stress. Experimental: completed either a cognitively or physically challenging task prior to providing a typing sample. The performance of each machine learning model was evaluated with three-fold cross-validation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. In Reference [10] sentence-level sentiment analysis based on a sentiment lexicon and sentence syntactic structures is performed on Chinese texts, and further used to calculate the aggregated sentiment of the document where the sentences are used to compute a weighted sum of the polarity of each sentence in the document, considering the importance of each sentence.