Sentiment Analysis: Using Artificial Neural Fuzzy Inference System

Sentiment Analysis: Using Artificial Neural Fuzzy Inference System

Syed Muzamil Basha (VIT University, India) and Dharmendra Singh Rajput (VIT University, India)
DOI: 10.4018/978-1-5225-3870-7.ch009


E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.
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This chapter aims to make the readers understand the theoretical foundations, algorithms, methodologies for analyzing data in various domains such Retail, Finance, Risk and Healthcare. To define the core objectives of any above mentioned businesses, one should first give an attempt to understand the customer profitable attributes in order to maintain successful customer relationship. In Understanding Customers - Profiling And Segmentation, our focus is to show on how importance is understanding customer, and discussed different techniques and applications, (Basha SM et al. 2017). Among them churn prediction in the mobile Telecommunication industry is one, in which the Life Time Value (LTV) of a customer is derived using Survival Analysis, model with their limitations are discussed in detail in section Churn Prediction in the Mobile Telecommunications Industries (Priss et al. 2006). The other real time application is Market Basket Analysis for a super market based on frequent item-set mining, in which data mining techniques are implemented to define new pattern by extracting associations from stores transactional data. Techniques like Apriori, K-Apriori and their detailed procedures are explained in Market Basket Analysis for a super market based on Frequent Item-set Mining. Where as in section Bankruptcy prediction for credit risk and different approaches like early Empirical Neural Network, Bayesian Network are discussed in detail, and also steps to be followed to design a model for supply chain risk propagation. In all the above application the domain and type of data varies. So, one should have good domain knowledge to perform prediction. Where as in section Text Categorization we continued our discussion on how to categorize text and the approaches to perform prediction on text data. In section Sentiment Analysis our discussion is on how to perform sentiment analysis on customers reviews and different classifier used like probabilistic, Naive Bayes, Maximum Entropy and Linear classifier like support vector machine, Neural Network, the decision tree. In addition to that other related fields where sentiment analysis is performed like Emotion detection, and prediction model for sentiment classification also discussed (Basha SM et al. 2017). With all the knowledge gained from the above sections, we identified few open problems in area of prediction which is domain specific and data centric like: Data problem, language problem. In section Open problems listed the open problems in Artificial Neural Fuzzy Inference system applied to the field of sentiment analysis on Text data. In section Artificial Neuro-fuzzy inference system (ANFIS) is a fuzzy system. In which, membership function parameters have been adjusted using Neuro-adaptive learning methods similar to those used in training neural networks, and also listed out steps to create, train, and test Sugeno-type fuzzy systems using the Neuro-Fuzzy Designer (Basha SM et al. 2017).

Key Terms in this Chapter

Decision Tree (DT): An iterative process of splitting the data up into partitions.

Natural Language Processing (NLP): An approach to analysis the text information.

Life Time Value (LTV): Is a straightforward measure that produce customer profitability and level of churn risk management at individual customer.

Survival Analysis (SA): It is a collection of statistical methods which model time-to-event data, the time until the event occurs is of interest.

Artificial Neuro-Fuzzy Inference System (ANFIS): In ANFIS using a given input/output data set, the toolbox function ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned using either a back-propagation algorithm alone or in combination with a least squares type of method.

Sentiment Analysis (SA): It is to measure the opinion of a user about, product, service, topic, issue, person, organization, or event.

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