## 2 Background and Related Work
### Artificial Intelligence and Sentiment Analysis  
Sentiment analysis is seen to be, in previous research realized in this domain, a very restricted NLP problem. For this kind of analysis, we need to be able to conclude if there are any positive or negative sentiments for each target sentence or phrase. However, despite these facts, the methods that were discovered in the research field are working correctly in terms of Information Retrieval. The main struggles identified at the moment are pointing on how to handle in the best way negations or names of entity recognition, as well as the challenges of being able to deal with irony or sarcasm [16; 17]. Other studies have proved that NLP needs to deal with different levels of analysis. Depending on the form existing for the target subject (either text, document, or maybe linked sentences), different NLP and types of sentiment analysis can be applied. Therefore, it is very important to distinguish the levels of the analysis that will determine the tasks of the analysis: document level, sentence level or entity level [18]. Many papers in this domain are therefore following the general strategies. For example, the papers detailed by [PERSON] and [PERSON] [19] proposed the use of lexical cohesion, hence the use of physical distance between the collocations in order to realize a rank of the documents. At the same time, [PERSON] further proposes one more method measuring the distance between subjective words [20]. Additionally, there are some other examples that apply the known supervised methods of Artificial Neural Networks and Support Vector Machines in order to classify sentiments [21]. [PERSON] et al. have also conducted a study on sentiment analysis for big data. They have worked on demonstrating an overview of the current updates in opinion mining. They discovered that sentiment analysis has become very popular in this research field, and they also stated that a lot of research has already been performed, even though we still have challenges pointing to unstructured data. Moreover, they have declared that the most repeated lexicon source is wordnet, and that supervised techniques provide more accurate results than the dictionary techniques [22]. [PERSON] and [PERSON] have also presented a survey on some other techniques of sentiment analysis using textual data. They have presented in their study advantages and disadvantages of each method in a summarized way, and the conclusion was that, in order to be able to perform sentiment analysis, multiple techniques could be used, some methods being based on lexicon, some using training sets, and some using both of them. The analyzed methods are specific to a domain, and most of them study English and Chinese languages. Therefore, very few studies have been conducted on sentiment classification for other languages [23]. Finally, [PERSON] and [PERSON] [24] also presented an important method of the dictionary-based approach. In this one, a set of opinion words are manually collected, and this set is grown by searching in different areas (such as the well-known corpora WordNet or thesaurus), so that other synonyms and possible other antonyms can be found. The new words are then added to the seed list and the next iteration is being triggered. All this process stops in the moment when no other words are found. However, this dictionary-based method has major disadvantages, such as the inability to distinguish words that express opinions from those that are related to a specific context. [PERSON] and [PERSON] used the exact same approach to identify sentiment sentences in the advertising domain. Their proposal in this new context was to have a syntactic parsing, as well as a sentiment dictionary, but also a proposed rule for topic word extraction, which finally showed effectiveness in the process of extracting and selection the advertising keywords [2; 25]. For the general classification of sentiments, we can use two of the most popular approaches: subjective lexicon and machine learning.