## 3 Detection Approaches Review
### Sentiment Analysis on Text
This work, even when addresses sentence-level and document-level sentiment analysis using sentences as atomic units for semantic analysis, is unable to perform a more fine-grained analysis, which is achieved with aspect-based sentiment analysis. For the case of aspect-based sentiment analysis, there are two main problems to address, which are aspect detection and sentiment classification. Aspect detection is the detection of aspects from the training data for the model and sentiment classification is the actual sentiment analysis on the aspects, to give a sentiment label to them. There are several approaches for solving both of those problems, and also hybrid approaches that try to assess them at the same time [32]. For the case of aspect detection, frequency-based methods use the terms found in the training corpus with higher frequency as aspects for the aspect set of the sentiment analysis model [11]. Generative models are used for detecting aspects as well, in Reference [12] Conditional Random Fields (CRFs) with a variated set of features are used. The frequency-based method generates aspects using the most frequent terms, while generative models use string tokens and their associated features for this matter. The frequency-based method presented in Reference [11] has slightly less precision but significantly more recall than the one achieved in Reference [12]. Sentiment classification is addressed with dictionary-based methods in Reference [11], where a dictionary is generated by propagating the sentiment of a set of seed words through WordNet synonym/antonym graph, counting only adjectives as sentiment words. Then the dictionary is used for calculating sentence-level sentiment analysis using majority voting of the adjectives detected in a sentence and found in the dictionary, with some refinements such as flipping the polarity of negated adjectives or multiple polarities finding when the number of positive and negative words found in a sentence is the same. Supervised and unsupervised machine learning methods are also used for sentiment classification. In Reference [13] a Support Vector Regression (SVR) model is used to find the sentiment score of aspects as a real number in the interval zero to five. In Reference [14] each aspect is used to find phrases that could contain sentiment, and then a label is assigned to them using a method from computer vision called relaxation labeling. Dictionary-based approaches use an existing set of labeled terms, while supervised and unsupervised machine learning options use different machine learning techniques to assign labels to new terms. The reported precision was higher for machine learning approaches in this case than the dictionary-based method, but the recall was similar, although it reached higher values in some cases for machine learning approaches. Hybrid approaches try to detect aspects and assign sentiment polarities to them at the same time. In Reference [15] a syntax-based method was used to extract other aspects from words associated with a sentiment, exploiting semantic relations. Generative models are also used, in Reference [16] CRF are used to relate sentiments to aspects, extracting information from the relations between words. Both of the hybrid methods reviewed shown a high precision compared to other reviewed methods, but only the CRF approach presented in Reference [16] reached high recall. Applying the criteria for inclusion/exclusion of works relevant to this review, the work in Reference [10] was selected for applying sentiment analysis to at sentence-level and document-level, while others were selected for illustrating aspect-level sentiment analysis, using different techniques, and thus giving a global insight on the state-of-art in these lines of work. In Reference [12] a frequency-based method is presented for aspect detection, while in Reference [12] CRFs are used. Those two techniques are different as one uses frequent terms, while the other extracts features using a model and features related to text tokens.