## 2 Related Work  
This section provides a comprehensive review of the recent studies, along with recommended methodologies for addressing sentiment analysis challenges based on word embedding and deep learning (DL) techniques. Next, the state-of-the-art literature is addressed, with a focus on sentiment analyses in different areas. Over the last two decades, the classification of user sentiments has attracted an increasing number of scholars and yielded a large number of research findings [10]. The classical machine learning and deep learning methods for classifying emotions mostly depend on supervised learning. The challenge is that natural language processing relies on efficient word embedding. By thoroughly training the global word-word co-occurrence of statistical data from the corpus, [PERSON] et al. [11] and [PERSON] [12] first revealed that word vectors are learned through an RNN. As seen in [13], the final global vector (GloVe) has an intriguing linear substructure in the word vector space. [PERSON] et al. [14] offered three models that took into account the text's emotional propensity and learned word embeddings with the sentiment. Word2 Vec embedding was used in [15] to perform a sentiment analysis on reviews received from the Indonesian website Traveloka. It is estimated that their model is 91.9% accurate. The authors of [16] presented a monitoring system based on DL and ontology to aid the traveling process. Fuzzy ontologies and Word2 vec embeddings were utilized to construct the suggested system's feature extraction module; the BiLSTM model was then used to classify the input text. According to Facebook, TripAdvisor, and Twitter data, the proposed technique was tested and found to be 84% accurate in its predictions. A multi-layer architecture for customer evaluation approaches (such as word embedding and compositional vector models) was proposed in [17]. A back-propagation technique was used to train the network and provide weights for the various aspects of the design once it had been integrated into a neural network. GloVe-DCNN, a brand-new device featuring a variety of sentimental qualities, was introduced in [18]. Word embedding,n-grams, and the polarity score properties of sentiment words were used to create a deep CNN. The authors of [19; 20; 21] developed a document representation system using the fuzzy bag of words paradigm (FBoW). An enhanced FBoW model that replaces the initial hard planning module with the Word2 vec approach using fuzzy mapping was developed by replacing the original module with the Word2 vec embedding. To determine the degree of similarity between words and clusters in seven different real-world document datasets, the researchers used three different approaches. For the identification and condition analysis of traffic accidents, the authors of another study proposed a system based on using ontology with LDA (OLDA) and a BiLSTM network [22]. OLDA was employed in the proposed system to extract data and label texts. As a result, classifiers such as FastText and BiLSTM are employed. This system was more accurate than the previous one. In another study, BiLSTMs were used to gather data on the long-term reliance on word and sentence locations [23]. A CNN and BiLSTM were combined in the suggested hybrid strategy. LSTM outputs from sentence classification are applied to the multi-channel CNN to produce n-gram features. To find ADRs (adverse drug reactions) in electronic medical data, the authors of [24] suggested using a deep learning approach (EHRs). The proposed approach used the joint AB-LSTM model and embeddings based on lemmas to locate ADRs. The proposed technique had an F-measure of 73.3% on the EHR dataset. The combined model, for example, outperformed previous models that used a stack of CNNs and LSTM deep learning models, as shown in [25]. The dataset representation of Word2 Vec is preferable to Word2 Seq. Sentiment-based and dictionary-based representations of texts are some of the ways that texts are encoded.