# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 1 Introduction
This increase could be explained via the preference of the USA-based Hispanic community to consume and create digital content in Spanish, rather than in English. In this way, regarding the use of Spanish on websites with multilingual content, Spanish is used on 4.1% of these websites, which places this language in fourth position, ahead of German and French [4]. The use of Spanish on social networks is also very high, and right now, it is the second most used language on digital platforms and social networks, such as YouTube, Facebook, Netflix, LinkedIn, Wikipedia, Instagram, and so on, after English. In particular, if we analyse the percentage of active social network users in relation to the total population in 2022, Spain was eleventh place with respect to the number of social network users, with 87.1% of its population using social network users, followed by other Spanish-speaking countries, such as Argentina, with 83.3% (thirteenth place), and Colombia, with 81.3% (twenty-third place) [1]. Regarding this, there are many studies that have focused on SA, but most of them are related to documents written in English. Although there are studies in Spanish on the SA of social networks [5; 6], more research is needed due to the high complexity of detecting, handling, and processing certain aspects of Spanish, such as negation, sarcasm, or ambiguity, which is even more complex in the context of social networks. These are, therefore, major challenges that are still open and need to be addressed. In SA, two main approaches have been combined for the task: (i) lexical-resource-based approaches and (ii) supervised-learning-based approaches. The first approach requires a dictionary of words associated with a sentiment (positive, negative, or neutral). Such dictionaries are compilations that capture prior knowledge of the words that appear in them. In supervised-learning-based approaches, no lexical resources are required, but a set of previously labelled examples of opinions is required. Finally, there are hybrid approaches that combine the two previous techniques. As for the first approach, the number of lexicons in Spanish is limited,and they need to be developed for each specific domain. Moreover, this approach must be combined with additional techniques to detect negation, sarcasm, or ambiguity in language, making them more complex strategies. However, the supervised learning approach needs a labelled corpus, but multiple machine learning algorithms can be applied to the same corpus with greater versatility. In terms of the effectiveness of these two techniques, some works show that supervised approaches could improve the accuracy of lexicon-based approaches in some cases and contexts [7]. However, in addition, of all the different machine learning models that can be applied, deep learning algorithms have been gaining prominence in recent years, as they can achieve better results than traditional models and are able to run faster thanks to the high performance of graphics processing units (GPUs). In addition, in deep learning approaches, the feature vector is composed of word embeddings (WEs) [8], which are much richer in word-related information than the features used in traditional algorithms, as they capture more information about similarity and semantic features of words. However, despite these important benefits, there are few proposals in Spanish for SA in social networks using deep learning, specifically in Twitter, and those that do exist have room for improvement in their accuracy performance. Therefore, we propose the design of a novel hybrid model based on deep learning strategies applied to a generic corpus of labelled Twitter datasets, which improves the accuracy of other existing models. The main objective of this study is to develop a deep learning model for sentiment analysis on Twitter using a labelled corpus from the Spanish TASS dataset [6; 9], which is part of the Spanish Workshop on Sentiment Analysis at Spanish Society for Natural Language Processing (SEPLN) [10].