## 1 Introduction
In LeSSA, we utilize high-quality wide coverage sentiment lexicons along with other machine learning techniques, such as k-mean clustering, self-training, co-training, active learning for training sentiment learners. The full-trained sentiment learners predict the sentiment polarity class for the test review document. We employ classification fusion using majority voting to predict the final sentiment label for the unseen textual reviews. In LeSSA, we aim to address the issues mentioned above under the umbrella of the two-step process (1) integrating several sentiment lexicons leveraging linguistic and semantic knowledge to induce high-quality wide coverage sentiment lexicon and, (2) training classification models based on high-quality training datasets for sentiment classification. In the former case, our objective is to induce a high-quality wide coverage sentiment lexicon. The existing general sentiment lexicons have some shortcomings. (a) Many of the existing general sentiment lexicons do not cover domain sentiment words, which may ignore important domain-specific sentiment words. (b) Mostly words that are not considered sentiment words in the existing general sentiment lexicon can express sentiment in a particular domain. (c) Most of the existing sentiment lexicons cannot handle effectively context-dependent sentiment words. In order to solve these shortcomings in the existing general sentiment lexicons, we integrate various sentiment lexicons leveraging linguistic and semantic knowledge to expand the sentiment lexicons for better sentiment classification. In the latter case, we competently select high-quality data for training to minimize the data labeling exertion for better sentiment classification. In order to select the high-quality training data, first, we generate the initial training set based on k-means clustering, we train first classifier on the initial training set and apply on unlabeled training dataset to label them based on some strategy (e.g., uncertainty sampling, high confident level). This process continues for several iterations until the termination condition is satisfied, and finally, the fully learned classifier is trained on the labeled training data. While training data we remove the noisy and redundant features and extract and select the most effective sentiment features for better sentiment classifier learning. Solutions to the mentionedissues result in lexicon-based and semi-supervised learning-based classification models. In detail, the final output predictions of all the designed and implemented classification models on test data are combined using majority voting to make a more accurate sentiment classification. The experimental results show that our proposed approach is suitable in predicting the true class that can improve the sentiment classification performance efficiently and outperforms baseline methods. The key contributions of this work are as follows:  
* A novel, unified sentiment analysis framework, known as LeSSA, has been proposed for textual sentiment classification. * The confluence of various sentiment dictionaries has been employed to generate a high-quality wide coverage sentiment lexicon. * We identify and extract context-aware domain-specific sentiment words by leveraging linguistic rules and semantic knowledge. * We exploit the bootstrapping method to obtain the most reliable training datasets employing three well-known semi-supervised learning techniques, such as self-training, co-training, and active learning in order to select the most useful instances to enhance and update the initial training set. * We extract sentiment bearing features and reduce the feature space while exploiting MRMR feature selection criteria. * We design and implement multiple sentiment learners. * The results obtained using classification fusion show higher accuracy comparable with base sentiment learners. * We evaluate the performance of various designed classification models in order to show their effect on the proposed unified framework.