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Natural Language Processing in Business Research Paper

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Updated: Feb 6th, 2022

Introduction

Natural language processing or NLP is a specific field of artificial intelligence that is focused on programming computers to analyze natural language data and be able to process it easily. The phases of NLP include analyzing each word’s structure, parsing sentences, and conducting semantic analysis (understanding the meanings of words and relationships between words) (Kalyanathaya et al., 2019). The next phases are discourse integration (linking new language content to what has been said before) and pragmatic analysis (establishing the actual meaning and goal of the text) (Kalyanathaya et al., 2019). Today, NLP positively affects business by giving rise to applications and software that reduce language barriers in international trade, handle some customer service requests, and let businesses benefit from commercial AI assistants.

NLP: History of Development and Purpose

The history of NLP can be traced back to the 1950s when the global scientific community became interested in exploring computers’ ability to demonstrate intelligent behaviors and imitate human thinking. The introduction of the Turing Test in 1950 (the test evaluated computers’ ability to support conversations with people and be mistaken for humans) is often referred to as the very start of NLP and AI (Kalyanathaya et al., 2019). Seven years later, Noam Chomsky revolutionized the field by introducing “a rule-based system of syntactic structures,” which further supported the development of NLP (Kalyanathaya et al., 2019, p. 199). Unlike many technologies that were initially expected to bring substantial profits, NLP was developed out of the need to further explore the uses of computers when working with texts, thus giving rise to helpful applications.

Positive Impacts of NLP on Business and Practical Implications

Although its ultimate goal (enabling computers to understand and generate adequate and meaningful speech) is a long-term one, NLP has partially fulfilled its promise in many fields of activity, including business and manufacturing. For instance, NLP has given rise to text mining software, machine translation applications, and systems to automate manufacturing processes (Kalyanathaya et al., 2019). Nowadays, NLP-based software is very common and continues to change some aspects of doing business, such as improving the quality of customer service.

NLP and applications based on it have a positive impact on businesses all over the world by facilitating business processes, such as communication in international trade. Machine translation was the most popular application of NLP in the 2000s (Kalyanathaya et al., 2019). Nowadays, machine translation systems are often used in online international marketplaces to reduce language barriers between sellers and customers and support the latter by translating product descriptions into the languages that they understand. There is evidence that the adoption of different forms of AI facilitates international transactions, thus ensuring the success of international economic activities. For instance, according to the statistical study by Brynjolfsson et al. (2019), the introduction of eBay’s improved machine translation system has caused a 10.9% increase in exports on eBay due to a better quality of product title translation. Apart from product titles, machine translation systems in online marketplaces translate product reviews, thus enabling customers to make well-considered purchasing decisions. Based on the evidence above, NLP and AI facilitate small-scale international merchandise transactions by reducing language barriers between trading parties.

The existence of NLP also allows tech giants to gain further recognition and generate profits by developing and offering NLP-based personal assistant applications. There are numerous popular voice-enabled personal assistants, such as Alexa, Cortana, Siri, and Google Assistant, and the market for such products is predicted to exceed $4.6 billion by 2025 (Tulshan & Dhage, 2018). According to surveys, the best applications are capable of assisting customers with almost 60% of daily tasks, such as finding restaurants and hotels or translating something into a different language (Tulshan & Dhage, 2018). The effectiveness of modern intelligent virtual assistants constantly attracts new users, thus increasing the market power of today’s tech giants even more. Although materials on how to build AI-enabled virtual assistants are found in open access, it cannot be denied that the development and testing of such applications require significant financial resources. With that in mind, it can be nearly impossible for smaller companies to benefit from AI and NLP by creating assistants that would be comparable to Siri, Google Assistant, or Cortana.

When it comes to business, customer service and enterprise chatbots can probably be called the most influential practical application of NLP. Chatbots or chat robots are computer programs that are capable of simulating human conversation by providing meaningful responses to voice or text messages by users. Unlike less sophisticated rule-based robots, AI chatbots based on NLP can understand the meaning of users’ requests and constantly learn from new information (Petouhoff, 2019). Thanks to their ability to learn, AI chatbots even make customers believe that they receive personal consultations from customer support specialists.

AI chatbots are becoming increasingly popular among different businesses that work directly with clients. For instance, according to the survey of more than 3000 service organizations conducted in 2019, 23% of respondents already had AI chatbots, whereas 31% were going to implement them within the following 1.5 years (Petouhoff, 2019). NLP-based chatbots are widely available nowadays, but the degree to which they are used depends on the industry. For example, as per the study by Salesforce, in the U.S., the industries in which such chatbots are used the most frequently include media and communications, technology, and financial services (Petouhoff, 2019). Although non-AI chatbots also have benefits in terms of customer service and can solve simple tasks and answer typical questions from clients, AI applications are often preferred due to the amount of flexibility that they offer.

Chatbots with NLP-powered functions positively affect business by automating many time-consuming but rather simple tasks that customer support specialists are responsible for. For instance, businesses commonly use AI chatbots to help customers to change their login details, check their account balances, or arrange appointments. It allows customer service agents to spend more time dealing with more complex issues, such as customer complaints. Based on the aforementioned survey conducted by Salesforce, more than 60% of customer service specialists having AI chatbots report being able to devote most of their shift time to “solving complex problems” (Petouhoff, 2019, para. 19). Therefore, due to AI chatbots’ contributions to communication with clients, businesses can significantly improve the speed with which they respond to requests from customers. Almost 80% of service organizations that have implemented AI chatbots report using them to provide customer self-service and collect information about more complex cases to facilitate agents’ work (Petouhoff, 2019). Thanks to these uses of chatbots, customers are not required to await their turn to be served or constantly repeat their details to any new agent working with them.

NLP and the Advancement of Social Causes

Apart from finding reflection in chatbots and other applications that businesses use to help their prospective customers instantly, NLP techniques can be utilized in research and opinion mining activities. It allows using NLP to improve an understanding of social issues that actually exist but have not been widely recognized yet. NLP methods can help to conduct sentiment analysis in many instances, thus allowing researchers to explore new social issues based on conclusions derived from very large datasets.

Additionally, to advance social causes, it is possible to program NLP-based conversational systems to provide adequate responses to inappropriate requests. For instance, sexual harassment is a social cause that can be advanced using NLP. Potentially, AI personal assistants can contribute to shaping negative attitudes to harassment by responding to sexual innuendos and verbal abuse in a way that would make users recognize the inappropriateness of such comments. According to the study of conversational systems’ responses to harassment, commercial NLP-based products, including Siri, Alexa, and Google Assistant, avoid engaging in conversations after receiving too many offensive requests (Curry & Rieser, 2018). Also, unlike other types of conversational systems, they give almost no responses that could be perceived as flirtation (Curry & Rieser, 2018). However, more training is required to make these applications fulfill their potential in promoting the moral standards of behavior.

Conclusion

NLP positively affects business since it enables prominent tech companies to maintain their leadership by developing voice-activated personal assistants that become enormously popular. Also, being used in machine translation systems, NLP helps to ensure mutual understanding between eBay sellers and buyers, thus facilitating global trade. NLP-based chatbots are used by customer service specialists in different industries. They benefit businesses by optimizing the workload on customer service agents, solving the issue of customer wait times, and helping to personalize communication with clients in an effective manner.

References

Brynjolfsson, E., Hui, X., & Liu, M. (2019). Does machine translation affect international trade? Evidence from a large digital platform. Management Science, 65(12), 5449-5460.

Curry, A. C., & Rieser, V. (2018). # MeToo Alexa: How conversational systems respond to sexual harassment. In M. Alfalo, D. Hovy, M. Mitchell, & M. Strube (Eds.), Proceedings of the second ACL workshop on ethics in natural language processing (pp. 7-14). Association for Computational Linguistics.

Kalyanathaya, K. P., Akila, D., & Rajesh, P. (2019). Advances in natural language processing – A survey of current research trends, development tools and industry applications. International Journal of Recent Technology and Engineering, 7(5), 199-201.

Petouhoff, N. (2019). Web.

Tulshan, A. S., & Dhage, S. N. (2018). Survey on virtual assistant: Google Assistant, Siri, Cortana, Alexa. In S. M. Thampi, O. Marques, S. Krishnan, K. C. Li, D. Ciuonzo, & M. Kolekar (Eds.), International symposium on signal processing and intelligent recognition systems (pp. 190-201). Springer Singapore.

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