# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 2 Related Works
### Sentiment Analysis Based on Lexical Resources  
As for techniques based on Spanish lexical resources, there are several opinion lexicons that encompass words associated with a sentiment or an opinion value (polarity). However, the amount of Spanish lexicons is quite limited. As far as polarity lexicons are concerned, we can consider the opinion lexicon developed by [9] or the iSOL [11], based on one of the most important English lexicons for the classification of polarity [12]. In addition, the Spanish adaptation of the Affective Norms for English Words (ANEW) is presented in [13]. In another work, the authors of [14] developed a lemma-level sentiment lexicon for several languages, including Spanish, generated from an improved version of SentiWordNet, a popular lexicon with positive and negative words. The authors of [15] have developed Semitext, a sentiment analysis software for Spanish, based on knowledge and supportedby several word databases. In contrast, other lexicons are based on emotions rather than polarity, such as the one developed by [16], which considers six emotions. However, using only opinion lexicons is not entirely effective for all domains, as each domain usually has a specific way of expressing a positive or negative opinion. Moreover, these lexical resources alone are not able to cope with the detection of negation, sarcasm, or ambiguity in opinions. Consequently, many authors focus on the processing of negation in Spanish. Thus, the authors in [17] adapted the SO-CAL sentiment analysis tool from English to Spanish [18]. This proposal consists of a lexical dictionary with positive and negative words together with the integration of rules and intensifiers to detect the degree of negation. Furthermore, the authors in [19] incorporated dependency-based techniques for negation detection to establish the scope of intensifiers and negation cues. This system achieves an accuracy of around 78% in detecting only two polarities (negative or positive), but it is applied in the context of product and service reviews, not on Twitter, where the language characteristics are different. In this sense, the authors of [20] have proposed rules based on dependency trees to identify the scope of the most important negation cues, defined by the Royal Spanish Academy, together with a lexicon-based sentiment analysis system for polarity classification. This system was applied to study the scope of negation in Twitter [21], reaching accuracies of around 62% when detecting three levels of polarity (positive, negative, or neutral). In the same line of work, [PERSON] et al. [22] developed an opinion mining system based on the ANEW Spanish lexicon applied to hotel reviews using the negation cues studied in [20]. Although the accuracy increases above 90%, it only focuses on detecting two polarities (negative and positive) and does not integrate the more difficult-to-identify neutral polarity. Furthermore, the system was developed in the context of hotel reviews, not in the more complex context of social networks such as Twitter. [PERSON] et al. [23] combined different methods to deal with negation by applying effects of negation, modifiers, jargon, abbreviations, and emoticons in sentiment analysis, reaching an accuracy on a Twitter corpus between 83 and 87% for the detection of two polarity classes: positive and negative. Other approaches take into account not only sentiment but also subject matter. Thus, [PERSON] et al. [24] have proposed a classification system for Spanish tweets by evaluating the use of stemmers and lemmatisers, n-grams, word types, negations, valence shifters, link processing, search engines, special Twitter semantics (hashtags), and different classification methods, where the highest accuracies are around 58% for topics and 42% for Twitter polarity detection.