## 1 Introduction
In order to improve the existing general sentiment lexicons, we integrate them leveraging linguistic and semantic knowledge to expand the word coverage of individual sentiment lexicon. Semi-supervised learning (SSL) is a machine learning technique that utilizes a large amount of unlabeled data along with a small amount of labeled data to learn classifiers and achieve a better performance [27]. In the literature, mostly learning models uses semi-supervised learning techniques, such as self-training, co-training, active learning, and a combination of these has been mostly used for textual sentiment classification [28; 29; 30; 31; 32; 33]. Self-training is the most well-known and simple SSL algorithm. In self-training, first, a classifier is trained on a small number of labeled data to predict the label for the unlabeled data. Then, high confidence level unlabeled examples with predicted labels are selected to add to the training data. Further, the classifier is retrained, and the process is recurrent until a predefined threshold is satisfied [34]. [PERSON] et al. [34] addressed a semi-supervised learning approach, self-training, for sentence subjectivity classification. [PERSON] et al. [14] proposed a self-training method for automatically domain-specific features acquisition by using the pseudo-labeled examples with high confidence. [PERSON] et al. [28] presented the SCLC Model (Self-supervised, Lexicon-based and Corpus-based model) for sentiment classification. Co-training is another semi-supervised learning method presented by [PERSON] et al. [35]. In co-training, two classifiers operate on two different and sufficient views for the training set expansion. In the standard co-training algorithm, each classifier classifies the unlabeled data and selects the most confident classified examples according to a threshold and adds into the training set of another classifier. The training set of each classifier is enriched, this process continues for several iterations until the condition criteria is satisfied [30; 36]. [PERSON] et al. [31] proposed co-training for semi-supervised sentiment classification based on dual view bag of words representation. [PERSON] et al. [37] proposed a semi-supervised model for sentiment classification based on the idea of combining lexicon-based learning and corpus-based learning in a unified co-training framework. Active learning, a type of semi-supervised learning, is used to actively select the unlabeled examples to be annotated by an oracle (e.g., a human annotator) with minimum annotation effort [38; 39]. The goal of active learning is to improve the classification accuracy with fewer labeled instances [40]. [PERSON] et al. [41] proposed a novel Active Deep Network (ADC), to address the sentiment classification problem with active learning using a small number of labeled data. [PERSON] et al. [33] proposed a novel learning model based on the combination of active learning and self-training for cross-lingual sentiment classification. They also designed and implemented some other baseline models and compared it with their proposed model to show the actual effects of different parts of their proposed model. In semi-supervised learning, the initial training set is selected randomly from the whole unlabeled data. Random selection of the initial training set does not guarantee to be the most representative subset of the entire unlabeled data. Besides, mainly existing semi-supervised learning methods, which utilize unlabeled data, contain abundant and redundant noisy features that may affect classification accuracy. The existing semi-supervised techniques cannot handle these noisy features effectively. Moreover, existing approaches do not focus effectively on the sentiment features of the unstructured data. In this paper, we propose a novel unified sentiment analysis framework for textual sentiment classification called LeSSA.