Natural language processing (NLP) has emerged as a pivotal technology in the field of artificial intelligence, revolutionizing how computers understand and interpret human language. As previously mentioned, in this series of articles, we aim to provide a non-technical introduction to NLP, explaining its significance, key concepts, and practical applications. 

Before diving deep into practical applications and significance of NLP we want to provide some technical details on NLP so that readers will be able to understand the use cases/applications and significance easily. Please note that these technical tutorials are written in non technical manner so that they are easily understandable even for individuals without a strong understanding of data science and machine learning concepts.

Let’s start with sentiment analysis. In Machine Learning, sentiment analysis is the process of categorizing texts or text blocks by its underlying mood, emotion or mentality. The goal of sentiment analysis is to understand the emotions conveyed through people’s words, which can be utilized for various purposes.  Imagine conducting a business or customer survey where users rate their experience from 1 to 5, with 1 representing extreme dissatisfaction and 5 indicating utmost satisfaction. While this method provides tangible feedback, it fails to capture the underlying sentiments of the customers. However, by incorporating user comments and employing sentiment analysis techniques, we gain insights into the emotional spectrum associated with the product. This analysis delves into the feelings expressed by customers, whether it be happiness, sadness, or frustration, thereby providing a nuanced understanding beyond numerical ratings. sentiment analysis can be applied to unstructured data sources such as social media comments, reviews, or any textual feedback, enabling businesses to gauge customer sentiment and make informed decisions. From a business standpoint, sentiment analysis serves various purposes, offering valuable insights into customer perceptions and aiding in strategic decision-making. Here are some usages of sentiment analysis from in business: 

1. Social media monitoring: We can use social media posts, comments and other form of text communication to gather insights
2. Customer service: customer service  satisfaction response and reviews to gain insights on product satisfaction and prospective        customers 
3. Investment analysis: We can make informed decisions on which company share to buy or sell based on sentiment analysis
4. Employee satisfaction survey and various other market researches. 

The importance of sentiment analysis is beyond business. To name a few examples of its broader usages: it enables us to assess people’s emotions regarding global events, gauge the popularity or polarity of socio-political movements, understand students’ reactions to course materials, and even discern an individual’s overall emotional state—whether they are happy, sad, or experiencing depression—by analyzing their texts, emails, blogs, or any written communication. 

While sentiment analysis focuses on detecting emotions within text, semantic analysis, another term in NLP, delves into uncovering the underlying meaning of text or collections of texts like paragraphs or documents. Although sentiment analysis and semantic analysis share similarities, they offer distinct perspectives in text analysis. While sentiment analysis interprets the emotional tone of text, semantic analysis goes further by grasping the deeper meaning and context of language. Although this article does not aim to explore semantic analysis in depth, it introduces the concept to underscore the importance of both sentiment and semantic analysis in NLP, which will be further elaborated in a future article.

Why are sentiment and semantic analyses important? Simply put, when conversing with someone face-to-face, you gauge the underlying meaning of their words through cues like facial expressions and body language. However, in text communication, such cues are absent. Sentiment and semantic analyses serve as the computer science or machine learning approach to discerning the underlying motives, agendas, and sentiments of individuals or groups. By employing these analyses, one can derive the subjective emotions conveyed by text, making sentiment and semantic analyses crucial not just in business but in every facet of life.

There are several types of sentiment analysis, including:

1. Binary Sentiment Analysis: This type of analysis classifies text into two categories: positive or negative. It’s often used for simple sentiment classification tasks where the goal is to determine whether the sentiment expressed in the text is positive or negative.
2. Multi-Class Sentiment Analysis: In contrast to binary sentiment analysis, multi-class sentiment analysis categorizes text into multiple sentiment categories. For example, sentiments may be classified as positive, negative, or neutral, or may include additional categories such as very positive or very negative.
3. Aspect-Based Sentiment Analysis: This type of analysis breaks down the sentiment expressed in text into specific aspects or features. It aims to identify the sentiment associated with each aspect individually. For example, in a product review, aspect-based sentiment analysis may determine the sentiment towards different features of the product, such as performance, design, or customer service.
4. Fine-Grained Sentiment Analysis: Fine-grained sentiment analysis provides a more detailed analysis of sentiment by considering nuances in the sentiment expressed. It may involve categorizing sentiments into more specific categories, such as very positive, positive, neutral, negative, and very negative.
5. Emotion Detection: Emotion detection goes beyond sentiment analysis to identify specific emotions expressed in text, such as happiness, sadness, anger, or fear. It aims to capture the underlying emotions conveyed by the text.

And this completes the introduction of sentiment analysis. In the next article we will learn to do sentiment analysis using Machine Learning models.