Sentiment analysis on micro-blog data using machine learning techniques - A Review

The user generated content in social networks, websites, ratings and reviews about product, people, places have become very beneficial for various fields to improve its growth. Sentiment analysis, which studies user generated content and find the correct sentiment polarity. Data collection, feature extraction, sentiment classification and decision making are the steps involved in this process. Machine Learning and Deep learning Methods are used to predict the sentiments. Large Input dataset from websites or microblog is taken as input and processed. There are some challenges in identifying accurate decisions. This Review Paper focus on recent studies about learning models such as Support vector Machines, Maximum Entropy, deep learning models and various methods which are used in solving sentiment classification, product review analysis, Text Processing and visual analysis etc.


Introduction
Nowadays, enormous amount of information is presented in webpages, databases, and blogs as online document.The unstructured form of data from the internet required to be changed well-structured data, toanalyze further. The Analysis process used in many fields such as Consumer information, websites, and Social Media [1].The goal of sentiment analysis is study user feedback and predict the sentiments. Usually, It defines opinion polarity, i.e., if somebody has positive, negative, or neutral opinion towards something. For example, (i) if a person plan for a trip to a particular place, he simply refer the online feedback of previous visitors for making the conclusion. (ii) If a person want to purchase a product, he reads online ratings and comments first, and take a decision to buy the product or not. The need of sentiment analysis is raised because requirement of analyzing and formatting the unstructured data in social media or websites . [2].The idea of sentimentanalysis is , without human intervention it determines the way of user reviews [3].  In social media, Twitter has made a big revolution, which is connecting millions of people worldwide through micro blogging service. The registered user of twitter can post short messages about anything, these short messages are called Tweets, which can have character-set length upto 280 .In the recent years researchers applied SA techniques with twitter comments and the results helpful in decision making.Machine learning techniques are applied by extracting the sentences and features. The polarity score has been assigned to the lexicons and sum of the polarity score is calculated from the overall values. [4]

1.1.Sentiment analysis with product review
The human's opinions, sentiments towards a product are being analyzed by the machine and the results encourage the purchasing decision of the customers .product review analysis is an entirely recognized field, where the customers can easily decide their product needs. Initially the product reviews will be taken as input text, and preprocessing is done .In this phase the input file is normalized, which ready for training process. This file is mapped with the saved features for training. After mapping the data will be fed into the different models and the performance of the models are analyzed as results. Several Machine Learning and are proposed in the past .Deep learning models, a division of Machine learning well suited for sentiment analysis with large dataset. The following is the basic procedure in sentiment analysis for product review.

Procedure for SA process
Step 1: Input text paragraph from dataset Step 2: Preprocessing the text. It includes removing stop words, removing numbers , expanding acronyms, replacing negation and removing URLs .
Step3 :Feature Extraction phase: Extracting words corresponds to adjective, adverb and verb.
Step 4:Selection of essential features for training the input data. Mapping the input data with selected features.
Step 5:The dataset can be divided 70% training and 30% testing.
Step 6: Training dataset to Machine learning algorithms or rain the model with Deep learning techniques Step 7: Test the model with different values.
Step 8: Result analysis with performance of the model.

Literature Review
In this section various studies about sentiment analysis by using MachineLearning and Deep Learning models are explained. Naive Bayes, Maximum Entropy, Ensemble methods, SVM,Decision trees, Stochastic Gradient descent are some of the familiar machine learning methods used in recent researches. .

Machine Learning Techniques
The Author [5,MonishaKanakaraj], analyzed Navies Bayes, MaximumEntropy, Support vector Machines algorithms in his work. Twitter dataset 7086 reviews from election dataset, 25000 reviews from movie dataset were taken as input for sentiment analysis. Randomized Trees classification method produces better outcomes . Author [6,Ana Valdivia ], applied sentiment analysis with Trip advisor; it is an American travel website company that provides user opinions about their hotels, restaurant and monuments. The analysis task categorized into three steps, polarity detection, Aspect selection, and classification. The author applied SAMs , SentiStrength, Bing,Syuzhet, and CoreNLP methods to analyze the user opinios.The result shows that Syuzhet detect at best 57.48 and 45.10 percent of negative feedback.In this paper [7, (Geetika Gautam)] Author proposed machine learning techniques for sentence classification and taken product reviews as input dataset from twitter data. The naïve byes method provides better result than maximum entrophy.SVM provides improved results in unigram model. The accuracy level is gained in semantic analysis word net to to 89.9% from 88.2%.

2.2.Deep Learning Techniques
In deep learning, data representation is referred by increasing the abstraction levels . [16].In the previous papers, there are various deep learning Architectures are Discussed,. Deep learning is used effectively in both supervised and unsupervised algorithms.Author [17,Islam.J], proposed a CNN framework for predicting the sentiments from social images. This is implemented by using s using Caffe and Python on a Linux platform. This model can be used in multimedia content to know user feedback, predictive modeling and advertising. A twitter dataset with 1269 images is taken as input and back propagation method is applied.The proposed model achieved better performance than existing systems. AlexNet was used in earlier systems and GoogleNet provides nearly 9% improved performance.
Theauthors[18, A. Severyn]implemented deep learning methods with twitter data. To initialize the word embedding neural language is used.Embedded words and parameters are applied to initialize the network, and training phase done by using supervised corpus Semeval-2015. Sentence matrix pooling, activations, convolutional layers and softmaxare the components used in proposed system. Deep learning technique is implemented in messagelevel, phrase level to predict polarity and better outcomes achieved.
The researcher [19, L. Yanmei] has explained about sentiment analysis with user opinions from a microblog. The user feedback on hot events analyses by using CNN . The corpus which has 1000 micro-blog comment divided into three labels. 274 for neutral , 300 for negative and 426 for positive emotions. The proposed work is compared with earlier models CRF, SVM, other traditional methods and performances analysed.The proposed model achieved the good performance in emotion analysis.

Conclusion
The need of normalizing the online data increases researches in field of sentiment analysis. It extracts the unstructured data from social media and examines the user opinions. Machine Learning and Deep Learning Techniques play vital role in pre-processing the texts, classification, training, testing the models and result analysis. The outcomes of the research used to know the user expectations and feedback about a product, place, or any common thing. This analysis provides the way for development in all the fields. In this paper various studies have been discussed about machine Learning and Deep Learning Models which are used in sentiment classification.

Analysis with future work
This paper explained about some recent researches in sentiment analysis . There are so many models established by using Machine learning and Deep learning Methods. NaiveBayes, Support Vector Machines. Maximum Entropy, Ensemble methods, Decision trees, Stochastic Gradient descent are most used machine learning algorithms. Convolutional neural networks-CNN, DBN,Recursive neural network -RNN,DNN,RNN are frequently used Deep learning Algorothms. Since Deep Learning models have more hidden layers, performs better than other Techniques. These models provide efficient training in supervised and unsupervised ways and also it performs automatic feature extraction. These models have some challenges, it requires large input dataset for better performance and it is costly IOP Publishing doi:10.1088/1757-899X/1049/1/012012 8 to train. The performance of the algorithm varies depend on the problem and the input dataset.As a future work product review analysis can be done by using Deep learning Methods and the accuracy of the outcomes can be shown better than previous models.