# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 2 Related Works
### Sentiment Analysis based on Supervised Machine Learning  
However, techniques based on supervised machine learning do not require lexical resources but a set of previously tagged examples of opinions. However, obtaining labelled examples is a costly task, as these examples are usually labelled manually; in addition, it is important to have examples for the classification domain in which it is applied. Thus, in Spanish, we can find in the literature some research studies that address this problem. In [27], [PERSON] et al. focus on sentiment analysis in text documents by applying a support vector machine (SVM) classifier. For training and testing, a dataset with positive and negative valence texts was used, based on the analysis from web pages with product ratings. Based on the rating of the text, it was resolved whether the text is positive or negative. The texts were divided into three groups: positive texts (P), negative texts (N), and texts with no defined valence NEU (neutral, not used). The recognition system was validated in four different languages, including Spanish. Regarding the results in the Spanish language, the classification accuracy was 93.23% when predicting P and N texts, not without considering the neutral class. Although this work shows interesting results, it was carried out with posts on web pages that have a different nature than Twitter posts as, for example, the size of the latter are limited to 280 characters. Thus, the use of language on Twitter has its own characteristics that differentiate it from other uses of language [3]. In addition, the samples database was categorised as P, N, and NEU based on starts and some thresholds. In contrast, our database is manually labelled via human effort. All these differences make it impossible to generalise their results to the SA of tweets in Spanish and justify the need to explore other alternatives for this purpose by applying Deep Learning. In a similar context, the authors of [27] proposed a linguistically independent text classifier based on convolutional recurrent neural networks. The classifier works at the character level instead of higher structures (words and sentences). The models were tested on the Yelp dataset (reviews and user data) and on a privately collected multilingual dataset, and the classification accuracy for Spanish texts was only 67.33%. Focusing on sentiment analysis of Spanish tweets, some research can be found in the literature. In [28], SA was carried out in the context of the Colombian presidential election of 2014. In this case, the corpus consisted of 1030 tweets tagged via humans with positive, negative, or neutral polarity towards each of the candidates. This process included a feature extraction preceded by a normalisation phase, after which a logistic regression classifier was used to assign a label class to each tweet. The results showed some difficulties in inferring the vote based on sentiment analysis of the tweets. In the conclusions, the authors argued that the obtained results showed that inference methods based on Twitter data are not consistent and that more work is needed to deal with the characteristics of the language. In this sense, our proposal tries to avoid the effects of feature extraction with the use of deep learning techniques. Deep learning makes problem solving much easier because it completely automates what used to be the most crucial step in a machine learning workflow, which is feature engineering [29], as they rely on the use of unsupervised pre-training features, the most commonly found of which are word embedding vectors [30]. More recently, the authors of [30] built the first Spanish corpus of sexist expressions on Twitter and applied different techniques, such as SVM, random forest (RF), long short-term memory (LSTM) networks and Transformer Bidirectional Encoder Representations from Transformers (BERTs), to construct a novel Transformer architecture that aims to provide very promising results in NLP.