# GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier
## 2 Literature Review
### Review Classification Using Sentiment Analysis  
Authors in [10] perform sentiment analysis on data containing reviews about different electronic products such as mobiles, laptops, and monitors, etc. User reviews are collected from Twitter for this analysis. A feature vector is created to handle emotional and misspelled keywords from the text. Feature extraction is done in two steps: first, all the Twitter-specific features including hash-tags and emoticons are extracted and added to the feature vector, after that, these features are removed from the tweets and uni-gram features are extracted from tweets. For classification SVM, Naive Bayes (NB) and maximum entropy models are used which show very similar accuracy for tweet classification into positive, negative and neutral classes. Similarly, authors in [11] performed sentiment analysis on Facebook posts. The research aimed at classifying the sentiments of students by analyzing the text that students posted on Facebook. Sentiments polarity is classified as positive, negative or neutral. Authors developed an application called SentBuk that retrieves users' posted messages including textual messages, comments, and likes, etc. on Facebook. Sentiment classification is based on a hybrid technique that combines the lexical and machine learning approach, whereby, the lexicon-based approach is used for pre-processing while the machine learning approach is applied at the classification level. The proposed approach achieves an accuracy of 83% and is very helpful to guide teachers of the students' current state of mind and support adaptive e-learning. Opinion mining is very important for analyzing public opinions about a specific product and predicting its future sale trends, so, many research contributions can be found which are dedicated to mining user reviews about products. For example, authors in [3] proposed a method of extracting the polarity of users' sentiments about products. They develop a method that can automatically discover positive, negative, and neutral reviews about any given product. The approach uses self-tagged reviews as the training data. The data chosen for testing is extracted from Amazon and C | net and provides quantitative and binary ratings. Authors suggest that metadata substitutions and variable-length features can increase the machine learning classifiers' accuracy. In the same way, authors in [12] perform sentiment analysis on Twitter tweets to check the effect of the average person's tweets on the fluctuation of stock prices of a multinational firm Samsung Electronics Ltd. The research presents an algorithm to give fast and accurate results for user sentiments based on the tweets. The proposed algorithm involves calculating the sentiment score based on the keywords found in the tweets and assigning a sentiment score to each tweet, whereby, the score can be 1, 0 or \(-1\) for positive, neutral and negative, respectively. Opinion mining is not limited to analyze the reviews for products alone, but it can be applied to predict users' sentiments in reaction to various events. For example, research [13] uses sentiment analysis on twitter reviews to predict public reaction about various events that occurred in the FIFA world cup 2014. Manually labeled data is used for training which is then used to find the correlation between the Twitter sentiment and a particular event. The model used is a Bayesian logistic regression that uses lexicons, uni-, and bi-gram features to detect subjective or objective tweets. The tweets are processed to represent the public sentiments towards unexpected events. Another research [14] proposes a novel approach based on SentiWordNet to carry out opinion mining using web data. The count of the score falls under seven categories: strong-positive, positive, weak-positive, neutral, weak-negative, negative, and strong-negative to test the efficacy of NB, SVM, and multi-layer perceptron.