# GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier
###### Abstract  
User reviews on social networking platforms like Twitter, Facebook, and Google+, etc. have been gaining growing interest on account of their wide usage in sentiment analysis which serves as the feedback to both public and private companies, as well as, governments. The analysis of such reviews not only plays a noteworthy role to improve the quality of such services and products but helps to devise marketing and financial strategies to increase the profit for companies and customer satisfaction. Although many analysis models have been proposed, yet, there is still room for improving the processing, classification, and analysis of user reviews which can assist managers to interpret customers feedback and elevate the quality of products. This study first evaluates the performance of a few machine learning models which are among the most widely used models and then presents a voting classifier Gradient Boosted Support Vector Machine (GBSVM) which is constituted of gradient boosting and support vector machines. The proposed model has been evaluated on two different datasets with term frequency and three variants of term frequency-inverse document frequency including uni-, bi-, and tri-gram as features. The performance is compared with other state-of-the-art techniques which prove that GBSVM outperforms these models. e 20202014 April 20202017 April 20202  
opinion mining and is practiced within text mining. Opinion mining aims to contrive a system which is used to extract and classify review and identify opinions within the text. Traditionally sentiment analysis classifies the opinions polarity into positive, negative and neutral classes [3]. The polarity is based on the emotions or specialized words that are present within users' reviews. These reviews possess a significant meaning for the public, as well as, private companies because of the information they contain. The reviews contain the likes and dislike about a particular product or service so hold potential information that can be used by companies to improve the quality of the products. It can further help to devise or revise policies about particular products. The textual comments are much more significant than the numeric score because they represent what people exactly comment about a particular product [4]. Even though this content is meant to hold meaningful information for governments, businesses, and individuals, this user-generated bulk content has to be processed using text mining techniques and sentiment analysis. However, this process is not a trivial task and sentiment analysis faces several challenges that become obstacles in analyzing the accurate interpretation and meaning of sentiments and detecting the suitable sentiment polarity [5]. The first challenge is to tackle the nature of reviews text which can be either semi- or unstructured. Since most of the users writing reviews are novice or non-expert and non-professional writers, so they do not follow any set rules to express their views which results in semistructured and unstructured data [6]. Similarly, domain dependence, review structure, and language semantics make the analysis more challenging. For a more detailed list of sentiment analysis challenges users are referred to [5]. Supervised, as well as unsupervised, machine learning techniques have been applied for sentiment analysis which extracts the meaningful information from structured and unstructured text data to aid the decision-makers. Supervised techniques have proven to be more effective in determining the polarity of the sentiments, however, they require large amounts of labeled data which is not easy to get [7]. On the contrary unsupervised techniques, though not superior, are still advantageous as they can work without the labeled data. Support Vector Machine (SVM), Naive Bayes (NB), and tree-based approaches are reported to show good performance in sentiment analysis [8]. The selection of appropriate feature set from the data is an equally important step like the classification model.