Introduction to Artificial Intelligence and Machine Learning
Machine Learning (ML) is a buzzword for many years, the motive for this might be the massive data generated by applications, and to increase computation power for better algorithms. ML is used to automate ordinary tasks to offer intelligent insights; businesses in each domain try to benefit from it. Customers may be by now using such devices. For example, to keep track of users’ fitness apps like GoodFit, Leap Fitness Step Counter, MyFitnessPal, or home automation like Google Home. Still, there are many more examples of ML are in use like image recognition, speech recognition, prediction, medical diagnoses, and financial trading.
Artificial Intelligence (AI) is all over us, “search by voice” in Google Chrome or “automotive navigation system” in cars. Even E-commerce sites like Amazon, Snapdeal, Flipkart, and Jabong suggestions are based on AI. Interacting with Alex, Siri, and Google Assistant is also a practice of AI. AI is a wide area of Computer Science; it marks machines act like human brains. AI is not just obeying instructions about instructions to drive a car. But an AI should work more effectively and independently like a human to make a decision.
Sentiment Analysis (SA) or Opinion Analysis (OA) composed of ML & AI algorithms is an influential tool to boost businesses’ brand value and profit from popular customer experiences. SA is an ML tool that analyses texts for polarity, from negative to positive. By preparing ML tools with textual instances of emotions, machines automatically acquire knowledge of detecting a sentiment without human involvement. To make it simple, ML permits machines to learn new tasks without being programmed. Therefore, this chapter aims to review artificial intelligence techniques in text and sentimental analysis. The rest of the chapter focuses on background
Introduction to Sentiment Analysis
Customer requirement often dominates businesses’ spare time to carry out effective marketing strategy, from the reviews and opponents’ strategy to items’ release in the markets. As we live in a ‘Digital Age’, companies are transforming their strategies such as new product launches and opinion gathering on social platforms (Zucco et al.2020). Microblogging and Social media web sources are the best sources for potential advertising to reach more groups. The rapid adoption of these groups in social media promotes new launches effectively. On other hand, monitoring and analyzing reviews of all the chats taking place on open forums and social platforms about a new launch was each company’s dream. Hence, these chats can be used as a valuable source for knowing the public pulse.
Using (OA), companies can discover the opinions’ conveyed by customers and evaluate customer reviews found in tweets and forums (Khan & Qamar, 2014). From outsiders’ terminology, it evaluates every text and recognizes if the opinion or sentiment is positive, negative, or neutral. For companies who want to know what the customers’ perception is and how they feel, sentiment analysis delivers strategic use from the extraction of online comments and interactions. Eventually, that’s massive unstructured data to process. To keep an eye on what customers, comment on new arrivals, we need to undertake AI sentiment analysis, which helps in the automatic recognition of emotion in review text and obtaining quick, actual intuition from huge customer data.
The occasion to provide sense to unstructured data using Natural Language Processing (NLP) will allow AI optimizes OA. Cognitive technology (CT) is a field of AI, which is very prominent to understand social language and the routines that customers convey themselves on online forums. Social language is an informal language that uses slang terms (like 2day, B4, ABT), acronyms (like BF, TY, PLZ), abbreviations (like RT, PM, LI), etc. Customers can also express emotions, needs, and preferences using symbolic gestures. Due to the advancement in CT, the underlying tasks enable machines to simulate humans. As AI becomes better-found at human conveying, businesses slowly adopting this technology where CT would be influential.