RESEARCH MOTHODOLOGY SZRZ6014 Dr. Farzana Kabir Ahmad Taqiyah Khadijah Ghazali (814537) SENTIMENT ANALYSIS FOR VOICE OF THE CUSTOMER

Introduction Sentiment analysis - stated opinion or feeling of certain documents, texts or speeches whether it is positives, negatives or neutral sentiments. Sentiment can be classified into emotional states for example happy, angry and sad. Task to find out the thoughts or emotion of the authors or speakers about specific entities. (Feldman, 2013).

Introduction Sentiment analysis available in text format and speech or voice In voice sentiment analysis, it is a part of speech analytics that emphasize on evaluating the emotional states in conversation. Two methods to analyze the audio speech of voice sentiment - Acoustic approach or Linguistic approach.

Introduction Voice of the Customer (VoC) is a term to define the customer’s feedback about their expectations and involvement of the products or services. Sentiment analysis being applied to report important issues whether it is positives or negatives, satisfied or unsatisfied of a certain products and services.

Background of Study Applied in many domain areas such as social media Other examples are in marketing, online shopping, and politicians. This research paper, the author will focus more on voice sentiment analysis The domain area, will concentrate in consumer feedback of a certain products, services of organizations.

Background of Study The acoustic approach to sentiment analysis is highlight on assessing the specific feature characteristics of the audio including tone of voice, pitch or volume, intensity and rate of speech. The aim of this research is to analyze the voice of the customer and provide the sentiment of their opinion.

Problem Statement Without the customer’s feedback they cannot identify what consumers want and like. Nowadays in a contact center environment it should be as important too as in online social media

Problem Statement The social media users tend to deliver their feelings into social applications are countless. Their feedbacks are not all can be heard by the organization. Have untrustworthy feeling towards the certain organization because of their opinion are disregard.

Significant of Studies Sentiment analysis important in order to increase profit and to improve the business. Customers may used contact center to convey their feelings of a certain products and services. Automated sentiment analysis to find and measure wide-ranging areas of concern. Data can then be drawn to understand the relationships between customer sentiment and operational efficiencies.

Significant of Studies By identify rate of speech a shock customer tends to speak faster, louder and higher in pitch. Sadness and despair are reachable as slower, softer and lower in pitch. Angry speaker tend to speak quicker, much louder and may increase the pitch of stressed volume.

Significant of study To analyze how far the contact center of the organization can improve their business by implementing sentiment analysis through the customer’s voice.

Research Questions What sentiment analysis to analyze and who are involve? How can we identify customers’ sentiment (positive or negative) from their opinions? How accurately can we capture that sentiment?

Research Objectives To investigate and analyze sentiment of voice of the customers (VoC) such as the products, services and sales person within an organization. To analyze the sentiment of customer’s voice by using acoustic approach. To evaluate the sentiment analysis technique.

Literature Review Analyzing sentiment of voice of the customers (VoC) Feldman (2013) stated sentiment analysis is “the task of finding the opinions of authors about specific entities” (p. 82). Often categories of sentiment classification are “positive, negative or neutral” (Kennedy, 2012, p. 435). In contact center, sentiment of the customers whether positive which it’s indicate satisfied or negative - unsatisfied about the products and services offered by the company.

Literature Review Using acoustic approach The acoustic method to sentiment analysis depend on calculating specific feature characteristics of the audio, tone, pitch, power and frequency of speech (Nexidia Inc., 2009,p. 3). Acoustic approach in speech analytic to determine the emotion of the speaker. Abd. Razak et al., (2003) indicate that “period, average energy and pitch can deliver some emotional content of a speaking” (p. 49). The data in the speech signal might be utilized to predict sentiment from short spoken review. (Mairesse, Polifroni & Di Fabbrizio, 2012, p.3)

Literature Review Evaluation of the method in the sentiment analysis Linear predictive coding (LPC), - an instrument generally as a part of sound signal handling and speech processing to shows the wavelength of a digital signal of speech in compact structure, utilizing the data of a linear predictive model. “The benefits of this technique are its capability to afford most precise estimates of the speech parameters, and in its high speed of computation” (Abd. Razak et al., 2003, p. 50).

Methodology Research Design The quantitative study has been chosen as a suitable research method. It study the behavior in a controlled environment Exploratory research Population/sample Contact center in business organization where customer’s view, questions and complained will be recorded every day. Sample Size Sample size of 250 customer’s voice database that had been recorded in contact center.

Methodology Data Collection Secondary data that will review available literature, contact center database of customer’s voice and also through online searching.

Methodology Data analysis/ Process Figure 1: Process of collecting data in contact center. (Source: UVM)

Methodology The propose method to perform sentiment extraction to analyze the customer’s voice is by using Linear Predictive (LP) analysis, (Abd. Razak et.al, 2003). Figure 2: Speech emotion recognition process (Source:EmoVoice)

Linear Predictive (LP) “Extracting the emotional features. Most robust speech analysis method for assessing the fundamental speech parameters such as pitch, formants, spectra, vocal tract functions and for representing speech by low bit rate transmission or storage” (Abd. Razak et.al, 2003, p. 50). The benefits of this technique are its capability to deliver enormously precise estimations of the speech parameters, and in its high speed of computation. For the purpose of analysis, the researcher will only focus on 3 emotions which are satisfied (positive), unsatisfied (negative) and neutral.

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