# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 1 Introduction  
Social networks have become the most representative tools of Web 2.0 (the Internet), allowing millions of users to post and share information in a fast and seamless way, permitting a continuous flow of information. According to figures from different studies [1], the latest data from 2022 state that more than 9 out of 10 Internet users already use social media every month, and the percentage of social media users amounts to 75% of the world's population. In this scenario, 17 social media platforms accounted for at least 300 million active users in January 2022. Facebook is the most used, with over 2.91 billion monthly active users, followed by YouTube with 2.562 billion users, and WhatsApp with 2 billion users. Twitter, which combines the features of blogging, social networking, and instant messaging, has about 436 million active users worldwide, standing out not only for its popularity but also for its great monetisation and business potential. As a matter of fact, Twitter seems to be the seventh-favourite social network among Internet users in the 16-64 years age group [1]. Although this social network has recently changed its name, we will keep the original nomenclature in order to facilitate the reading and understanding of this article. In any case, due to the impressively growing amount of information on the Internet and social networks, as well as the large number of sources and the high number of opinions about any given content, it is essential to have automatic methods that allow us toclassify and analyse information quickly and efficiently; all this is used to feed any decision-making system. Natural language processing (NLP) seems to be a more rational choice to tackle complexity in the area of artificial intelligence (AI) [2]. The well-known specific fields required in this area are opinion mining (OM) and sentiment analysis (SA). These fields combine NLP and computational linguistics, which involve uncovering words and contexts to understand the opinions they reveal. More precisely, SA deals with determining the emotional tone behind a series of words in order to classify them via their polarity (positive, negative, or neutral) or emotion (sadness, joy, disappointment, etc.). Additionally, contents on social networks such as Twitter have different natures depending on the language employed, which has its own peculiarities, making it different from other uses of language [3]. In fact, some important problematic features of tweets are related to their short length (280 characters), the data sparsity, the lack of context, the low concern for grammar, and the use of an informal linguistic style (idioms, slang, and abbreviations). These characteristics make it difficult to integrate fully effective SA systems into social networks such as Twitter. Moreover, the application of SA in social networks is very useful in many cases, such as measuring the impact of social media actions, helping understand what consumers think about brands/companies, knowing what users think about certain topics, making better decisions on marketing strategies for product/service development, following trends in real-time, or even assisting in predicting the behaviour of users. For these reasons, the application of NLP strategies and SA to social networks is an active area of applied and basic research in order to make social networks and the web more usable and profitable. However, the rise in the use of Spanish continues to increase each day. Currently, it is the second most widely spoken native language, with nearly 493 million native speakers. Therefore, the Spanish-speaking population occupies an important place in the dissemination of information worldwide; about 7.9% of Internet users usually communicate in Spanish, so Spanish is the third most used language on the Internet, followed by English and Chinese [4]. The use of Spanish on the Internet is also growing; for example, in the period from 2000 to 2020, it experienced a growth of 1511% compared to the 743% increase recorded for English.