# Exploiting Stacked Autoencoders for Improved Sentiment Analysis
###### Abstract  
Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the performance of deep learning models depends on the values of the hyperparameters. Determining suitable values for hyperparameters is a cumbersome task. The goal of this study is to increase the accuracy of stacked autoencoders for sentiment analysis using a heuristic optimization approach. In this study, we propose a hybrid model GA(SAE)-SVM using a genetic algorithm (GA), stacked autoencoder (SAE), and support vector machine (SVM) for fine-grained sentiment analysis. Features are extracted using continuous bag-of-words (CBOW), and then input into the SAE. In the proposed GA(SAE)-SVM, the hyperparameters of the SAE algorithm are optimized using GA. The features extracted by SAE are input into the SVM for final classification. A comparison is performed with a random search and grid search for parameter optimization. GA optimization is faster than grid search, and selects more optimal values than random search, resulting in improved accuracy. We evaluate the performance of the proposed model on eight benchmark datasets. The proposed model outperformed when compared to the baseline and state-of-the-art techniques. data mining; natural language processing; text mining; text analysis; web mining +
Footnote †: journal:  
Both supervised and unsupervised machine learning algorithms can be employed for SA. The difference between these types is that the supervised algorithm needs a labeled dataset. An unsupervised machine learning algorithm, on the other hand, can analyze a dataset that has not been labeled. A supervised machine learning algorithm will produce results based on previous learning. It is extensively used for deciphering real-world computing problems. It is not possible to construct a large, labeled dataset for classifying text. Therefore, unsupervised machine learning (ML) is the best choice for textual data analysis. These algorithms can determine the hidden structures and associations in data. Combining two or more ML models and deep learning (DL) for the purpose of sentiment analysis can lead to great improvement in performance. DL algorithms are advanced machine learning techniques, as they are inspired by the human brain. DL algorithms can process a large volume of data with little human effort. In the case of ML, human input is necessary to rectify possible mistakes, while on the other hand, a deep learning algorithm can correct itself by learning through algorithm chains. The training stage of a deep learning algorithm takes a more prolonged length of time, but once it is trained it can solve intricate problems. Various studies have used DL models for the task of SA. For example, commodity market SA [1], financial SA [2], aspect-based financial SA [3], investor sentiment analysis [4], biomedical SA [5], clinical analytics SA [6], and social media SA [7; 8; 9]. Both deep neural networks and shallow neural networks can assess any task. However, an exclusive advantage of the deep neural network over the shallow neural network is that it can perform feature extraction along with learning tasks. This is due to intermediate hidden layers [10; 11] being used to extract better features. In contrast to deep neural networks (DNNs), which typically have multiple hidden layers of varying sorts, shallow NNs have only one hidden layer. DL algorithms can achieve an identical level of accuracy more efficiently in terms of a number of parameters and computations. DNNs learn a new and abstract representation of the input, at every layer, for creating deep representations. Every single deep learning model has its pros and cons, and can be efficiently applied in a single domain. A hybrid strategy, a means of merging two or more models, was proposed by [12; 13] for obtaining the benefits of both models and overcoming their flaws.