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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal (International Journal) ISSN-2456-1037

Vol.04,Special Issue 05, (ICIR-2019) September 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

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A REVIEW ON CLASSICAL AND MODERN METHODS OF SENTIMENT ANALYSIS Mohammad Danish

Research Scholar (Ph.D), College of Engineering Department of Computer Science & Engineering Dr. A.P.J Abdul Kalam University, Indore (M.P), India-452014

Abstract - In today's world of social media where people prefer to point their views on various social platform, sentiment analysis is the best technique to analyze people's opinion and choices for modern recommendation. Most of the people are proactively involved in posting comments and views regarding different issues. These issues may be related to some product, politics or environment. Sentiment analysis is an easy way to predict the possible outcomes of such issues. Sentiment analysis was earlier notified as opinion mining where it was limited to certain extent, however in modern computing it has broad dimensions and major applications to various domains. Sentiment analysis was earlier computed using some classical methods which were quite effective in certain fields with limited accuracy. Now a days there has been an enhancement introduced in these methods which involves the majority of machine learning algorithms responsible for improving accuracy than classical methods. In this paper we will discuss some traditional methods along with some modern techniques to explore the hidden potential of sentiment analysis applicability to computing and recommendation systems. It produces the polarity based outcomes which are quite easy to predict and to compute. Sentiment analysis works around three essential parameters of polarity, subject and opinion holder.

Keywords: Sentiment analysis, Machine learning, Recommendation, Computing.

1 INTRODUCTIO

Sentiment analysis is the study of analyzing the people's attitude and emotions for a particular entity. These entity may represent some individual, any event or some real time topic . Majority of the social media platforms are flooded by such different topics of people's interest.

People raise their voices and express their

views either in terms of reviews or comments [1,2,3]. The major part of the sentiment analysis is to extract the people's opinion in text and classify their polarity as labels accordingly. Following figure (Fig-1) depicts the classification process of sentiment analysis in general way.

Classification in sentiment analysis is done at three levels: document level, sentence level and aspect level. The main aim of document level is to classify the opinions extracted from the documents as

a positive or negative. In this methodology the entire document is considered as a basic unit of information and the output generated is also considered as a single entity. On the other hand at sentence

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2 level the major objective is to parse the sentiments at sentence level or it can also be defined as sentence analysis in each sentence. Prior classification it is important to identify whether sentence is subjective or objective. Wilson et al. [2]

finds out that sentiments related expressions are not subjective in nature.

Aspect level classification deals with sentiments analysis with respect to aspects of entities. Sometimes each sentence generate more than two aspect and outcome of polarity could be of dual nature. For example, "The student is not good at English, but he will have bright future". All of these methods are engaged according to use of organisation and the extent at which the sentiments analysis is to be discovered [4,5].

2 CLASSICAL METHODS A. Bag of Words

One of the most promising methods of NLP in sentiments analysis involves Bag- of-Words. Generally a bag of words consists of all unique words in a document and their frequencies of occurrence. In the language of mathematics you can consider it as the set of words which do not have duplicates value. Usually we avoid the sequences of words and focuses on the frequency of repetition of these words [7]. Such representation need a training set to identify the polarity of each sentence in a review of document and it is called a Corpus.

Once the corpus is ready then it becomes quite easy to identify the polarity of target review and its polarity. On the behalf of compared value we can easily define the predicted outcomes. These outcomes may be differ than actual status, so for every outcome we measure the accuracy factor for every output set corresponding to every input set [8,9].

B. Stemming

Stemming process involved the cutting off the end and beginning of the word considering the list of common prefixes and suffixes that may be found in an inflected word. The stemming approach works for the maximum events along with some limitation. Under mentioned are few examples of stemming [13].

C. Lemmatization

Lemmatization process involves the morphological analysis of words. To proceed such activity we need detailed dictionaries so the necessary algorithm need to create a link to form back to its lemma. Following is an example of lemmatization [15].

Above table easily depicts the word form and morphological information derived from it. Finally we can easily detect the lemma as final output.

3 MODERN METHODS A. Machine learning approach

Modern methodologies involved machine learning where the predicted output value is better than classical based methods. In this approach we usually use the supervised learning techniques are which are quite effective due to availability of highly trained data set. Another approach is probabilistic classifier which consists of mixture models. some of important classifiers are as follows [17,18]:

3.1 Naive Bayes Classifier

The Naive bayes classifier is a simple technique which is commonly used in initial phases. It usually computes the posterior probability of class .

3.2 Bayesian Network

A Bayesian network model is a acyclic graph whose nodes are variables and edges are presented as conditional dependencies. During the text classification computational complexities in Bayesian network become quite expensive [19].

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3 3.3 Support Vector Machine Classifier (SVM)

SVM are best suited for text based classification due to the sparse nature of text where few are irrelevant in nature and are closely correlated to each other.

SVM are used in text classification and for categorizing the reviews based on their quality[15,18].

3.4 Neural Network

Neural networks uses the neuron structure for counting word frequencies in the document. Linear function of a neural network is Pi=A . Xi. [22] One of the advantage of using neural network is that it minimizes the error a lot in comparison to other techniques.

3.5 Decision Tree Classifier

Decision tree classifier provides a hierarchical decomposition of the training

data space

in which a condition on the attribute value is used to divide the data. The division of the data space is done recursively until the leaf nodes contain certain minimum numbers of records which are used for the purpose of classification [21].

3.6 Rule Based Classifiers

In case of rule based classifiers the data space is provided with a pre defined set of rules. The left hand side represents a condition on the feature set expressed in disjunctive normal form while the right hand side is the class label. Lot of disperse methods are available to generate the rules. The major and two common criteria are support and confidence. The support is the absolute number of instances in the training data set which are relevant to the rule. The Confidence refers to the conditional probability that the right hand side of the rule is satisfied if the left-hand side is satisfied.

3.7 Reinforcement and Unsupervised Learning

Using a pre defined labeled training data set is easy to use to accurately classify the unknown data. However sometime we encounter certain data sets where no training data set is available to us. Such kind of learning is known as unsupervised learning. In this approach un- classified documents are arranged in

clusters where certain polarity is analyzed[24].

B. LSTM Approach

Long Short-Term Memory Recurrent Neural Network model is very popular to solve natural language based problems. It reads the sentence from the first word to the last one. And it tries to figure out the sentiment after each step. For example, for the sentence “The food sucks, the wine was worse.”. It will read “The”, then

“food”, then “sucks”, “the” and “wine”. It will keep in mind both a vector that represents what came before (memory) and a partial output. For instance, it will already think that the sentence is negative halfway through. Then it will continue to update as it processes more data [23].

3.8 LSTM Network

This RNN structure looks very accurate for sentiment analysis tasks. It performs well for speech recognition and for translation. However, it slows down the evaluation process considerably and doesn’t improve accuracy that much in our application so should be implemented with care.

4 CONCLUSION

This paper has presented few basic classical and modern methods of approaches used in sentiments analysis and in Natural language processing. Lot more methods are yet to explore especially in field of statistics.

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