# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
## 2 Related Works
### Fairness in Sentiment Analysis  
Sentiment analysis is a powerful analysis tool for extracting high-value information from huge and rapidly time-varying sources. Like all approaches based on machine learning and artificial intelligence, sentiment analysis algorithms must offer an adequate level of _fairness_[43; 44; 45; 46], defined as the property that ensures that a model does not generate outputs that may discriminate (or harm) individuals or groups of individuals because of sensitive information such as race, gender, religious conventions, or disability. A system that tends to discriminate against some groups over others is defined as _biased_.  
Building sentiment analysis models that ensure fairness is a moral obligation for the researcher; in addition, many countries around the world are actively working to adapt their legislation to penalize software systems that violate fairness. Beyond moral and legislative obligations, ensuring fairness has deep consequences on the success of an ML model and contributes to better acceptance of the system by its users.  
Unfairness and bias can have many causes, and among them, we mention _data imbalance_; that is, some segments of society may be underrepresented compared to others. Several empirical studies assessing the fairness of certain sentiment analysis approacheshave been proposed in the literature. A further source of bias arises from _data preprocessing_: at this stage, in fact, human choices (e.g., to include or exclude certain samples from the training set, to include/avoid some features, etc.) could exacerbate the bias in the source data. Natural language processing techniques could be biased since they could favor certain languages and dialects over others [47]; finally, any errors on the labels attributed to the sentences to be analyzed could affect the behavior of the sentiment analysis algorithm [48].  
In general, measuring the fairness of a sentiment analysis model is very difficult since the evaluation depends on the application context, and certain features used in the training of the model could be sensitive in a specific application domain and non-sensitive in other domains.  
The process of measuring the level of fairness of a system and improving its fairness must be considered an iterative process. To this end, it is necessary to ensure that the data collection process is _rigorous_, and it must eliminate noisy and missing data. To cope with unbalanced data, one must think of ad hoc strategies such as random undersampling or oversampling or, alternatively, data augmentation strategies that improve the quality of the training set (see [49] for a detailed review).  
Some authors (see [50]) suggest combining the output of multiple commercial sentiment detection APIs to avoid gender bias. A further interesting option is proposed in [51]; here, the authors carry out adversarial pretraining on several models, and they recommend retraining a sentiment analysis model if unfairness is detected.  
Despite the considerable scientific efforts that have been made, we believe that the problem of fairness still needs to be studied.  
Another issue with sentiment analysis is the potential for privacy breaches. In fact, the content of even a short text message can reveal information such as an individual's gender, political orientation, and mood. Several methods have been studied to anonymize data for sentiment analysis purposes, including differential privacy, bloom filters, or a combination of both [49; 52; 53; 54].  
This paper mainly studies the combination of intelligent optimization algorithms to improve the performance of machine learning models and ensemble learning. Based on the adaptive search advantages of intelligent optimization algorithms, a CS-SVM classifier (which can effectively mine text features and adaptively find an optimal hyperplane partition) is proposed.