# Social Network Sentiment Analysis Using Hybrid Deep Learning Models
## 2 Related Works
### Sentiment Analysis based on Supervised Machine Learning
The proposed methods achieved an accuracy between 61 and 74% in the automatic detection of sexist behaviours. However, this corpus is not oriented towards polarity detection as it focuses on the detection of sexist behaviour on Twitter. In a similar context, research can be found in [31], where the authors developed a proposal to detect hate speech in Spanish tweets, using BERT. The results allow distinguishing between non-aggressive and aggressive tweets (two classes) with an accuracy between 79 and 86% on different datasets. However, the authors of [32] have proposed an approach for annotators to reach a consensus in the process of annotating comments on Spanish social networks. They built a corpus with 3259 Spanish comments (P, N, and NEU) andapplied several classifiers for sentiment analysis detection, achieving 70% as the average F1-Score with multilayer perceptron. In the context of the Workshop on Sentiment Analysis at SEPLN (TASS), studies focusing on SA in Spanish tweets can be found [6; 9] using the TASS corpus. For example, in [33], a set of classifiers based on SVM, convolutional neural networks (CNNs), and LSTM were used over three variants of Spanish. The achieved results show the best accuracies around 59%, 59.2%, and 54.9% for the SVM, CNN, and LSTM algorithms, respectively. Similar results were reported in [34; 35]. Another approach using SVM was implemented in [36] for SA on Spanish tweets, achieving an accuracy of 62.88% when predicting five levels of polarity (P+, P, N, N+, and NEU) and 70.25% for three levels (P, N, and NEU). Furthermore, the authors of [6] have proposed two deep neural network models (CNNs and dense neural networks) integrating a Gaussian noise layer for tweet polarity classification in a Spanish Twitter corpus, achieving an accuracy of only 57%. Unlike previous works that have focused on testing the effectiveness of different neural network models separately, our work aims to address SA by using a composition of different types of deep neural network layers in what we call a hybrid deep learning model. The current state of sentiment analysis systems on Twitter continues to advance with the integration of hybrid deep learning architectures, a trend supported by recent research. In [37], [PERSON] et al. propose a hybrid architecture that combines the power of bidirectional recurrent neural networks (Bi-RNNs) and other techniques to enhance the efficiency and accuracy of sentiment analysis on Twitter data in an Arabic benchmark dataset. Bi-RNNs are employed to capture contextual information and sequential dependencies within tweets, which is crucial for understanding the nuances of sentiment in short, text-based social media content. In a 2022 study by [PERSON] and [PERSON] [38], a hybrid model combining a bidirectional long short-term memory (BiLSTM) neural network and a convolutional neural network (CNN) showed remarkable improvements in accurately analysing text emotion compared with a single CNN model. Furthermore, the impact of Transformer-based models in sentiment analysis, as highlighted in a study by [PERSON] et al., in 2023 [39], remains significant. BERT (Bidirectional Encoder Representations from Transformers) and its variants continue to be influential in leveraging contextual embeddings. The incorporation of ensemble strategies, such as those discussed by [PERSON] et al. [40], play an essential role in these contemporary hybrid frameworks by combining predictions from multiple models to improve sentiment classification accuracy. These recent articles collectively highlight the dynamic and innovative landscape of hybrid deep learning approaches in tweets sentiment analysis.