Abstract
The rapid transformation in the business domain enhances the understanding that to achieve competitive advantage, corporates need to understand customer sentiments. The abundance of customer data as customer feedback, product reviews, and posts on social media platforms provides an in-depth insight that can navigate strategic decisions and inflate customer experiences. In this context, the unification of machine learning and sentiment analysis emerges as a potent combination for extracting emotional traces from volumes of unstructured text data. This chapter searches into the sphere of analysis techniques of sentiment analysis for analyzing customer feedback, where the convergence of advanced machine learning techniques with sentiment analysis methods empowers businesses to derive valuable insights from the feedback gathered from various touch points. By decoding sentiments and opinions hidden within textual data, this approach enables organizations to capture a clear view on customer satisfaction, identify their pain points, uncover emerging trends, and tailor offerings accordingly.
Top1. Introduction
In an age where customers with digital voices are louder and more influential than ever, this association of machine learning and sentiment analysis emerges as a compass for guiding businesses to have a deeper understanding of their clientele. As we navigate through the chapter, we will explore the foundations of sentiment analysis and its significance in the modern business landscape. We will probe into the elaborate working of machine learning algorithms that drive sentiment analysis, unfold their capacity to comprehend the complexities of human expression. Additionally, we will examine various aspects different collaborative approaches with real life applications.
1.1 Understanding Sentiment Analysis
Sentiment analysis, alternatively known as opinion mining, is a natural language processing (NLP) approach that is used to recognize the emotional tone expressed by a customer in a text or other forms of communication. Its objective is to automatically categorize the sentiment of the text, labelling it as positive, negative, or neutral. In some cases, it goes a step further by classifying sentiments into specific categories like “happy”, “angry”, or “sad”. This technique is widely used across various industries for understanding customer opinions, market trends, social media sentiment, and more. Figure 1 explains the sentiment analysis (Tonic, 2018).
Understanding customer sentiment
Understanding consumer behaviour is a crucial aspect for businesses in every industry. Customer reviews are on various E-com and social media platforms are a sizable source to know customer sentiments. Various research has been conducted to analyse customer reviews and understand what are the pain points, what factors impact customer satisfaction and recommendation. A study focused on airline passengers' online reviews and found that factors such as seat comfort, staff, food and beverage, entertainment, ground service, and value for money significantly influenced customer satisfaction and recommendation (Ban & Kim, 2019). A different research investigated customer worries about privacy in the retail sector, highlighting factors like retail platforms and the sensitivity of data. These factors were found to influence how privacy concerns affected outcomes in the retail industry (Okazaki et. al., 2020). Additionally, researchers have explored the effect of product reviews, prices, transaction trust, and security on online purchasing decisions among the Islamic millennial generation (Rokhman & Andiani, 2020). Furthermore, the use of machine learning and text analytics has been proposed to classify review sentences as praise or complaint and gain valuable insights from customer reviews (Khedkar & Shinde, 2018). Overall, these studies contribute to the understanding of customer reviews and provide insights for businesses to improve customer satisfaction and make informed decisions (Davis & Tabrizi 2021). Therefore, to enhance profitability, sustainability and achieve competitive advantage businesses seek indebted knowledge of customer sentiment in true sense.
2. Approaches Of Sentiment Analysis
Sentimental analysis referred as opinion mining which explore, analyse, and organise human feelings (Anand,2021). It involves extracting feelings using NLP technique from various sources such as images and text and aims to determine and categorize the emotional state, attitude, and expression of individuals in different situations (Srinivasan & Subalalitha, 2021). Sentimental analysis is used in various fields, including research, decision support, and market analysis (Sainger, 2021, Bhargava & Rao, 2018). The analysis can be employed across various domains to different types of data, that includes social media posts, reviews, and market trends. The goal of sentimental analysis is to classify and understand the emotions and sentiments behind the text or visual data, providing valuable insights for businesses and researchers. Analyzing customer feedback using big data and machine learning techniques can provide valuable insights into customer preferences, sentiments, and behaviour.
Key Terms in this Chapter
Unsupervised Learning: In contrast to supervised learning, unsupervised learning algorithms learn patterns exclusively from unlabelled data.
Big Data: Primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing techniques.
Supervised Learning: It uses labelled datasets to train algorithms to classify data or predict outcomes accurately.
Sentiment Analysis: Known as opinion mining, is a natural language processing (NLP) approach used to recognize the emotional tone expressed by an individual in some form of communication.
Data Preprocessing: Text preprocessing that includes tokenization, stopword removal, and stemming, and is crucial for feature extraction.
Semi-Supervised Learning: This type of machine learning method merges aspects of both supervised and unsupervised learning. It uses a combination of a small amount of labelled data and a large amount of unlabelled data to train models.
Machine Learning: It is a field of study in artificial intelligence that tries to make machines learn automatically without explicit programming.