Artificial Intelligence Processing of Natural Languages: Improving Understanding and Communication

The goal of natural language processing (NPL) is to give machines the capacity to comprehend and interpret natural human language. Liu et al. (2023) assert that the convenience of machine-to-machine communications has been facilitated by NPL. Therefore, it is possible to consider that AI systems could enhance the naturalness and intuitiveness of interactions by analyzing, understanding, and creating language that is similar to that of humans. Many problems related to the use of NPL were found when the previous literature was analyzed. According to Dreisbach et al. (2019), cost effectiveness is a significant element influencing the implications of non-performing loans (NPLs). Furthermore, the current use case for the same is restricted. Therefore, when implementing NPL, ROI is a crucial component that must be taken into account. NPL models are still in the development stage, thus there's also some risk involved with the implications of the same.

The study's Figure 1 shows the expected worldwide income for the NPL market. The statistical data indicates that the value of the global non-performing loan (NPL) market in 2017 was 3185.7 billion USD (Statista, 2022). In addition, according to Statista (2022) the market grew to 5075 billion USD in 2018. Forecasts for the market's growth indicate that the non-performing loan (NPL) market will reach a valuation of $43283.9 billion by 2022 (Statista, 2022). One could speculate that a slow and steady expansion of the market is anticipated. Such a trend of expansion supported the study's logic and goals.

Aim: The primary aim of the study is to discuss the possibilities of NPL in order to enhance communication and understanding.

The cornerstone of NPL was laid with the introduction of AI, according to the examination of previous literature. As to Khurana et al. (2023), Natural Language Processing (NLP) draws inspiration from linguistic theories that explain the structure and meaning present in human language. Furthermore, natural human speech serves as the inspiration for the fundamental concept of adding NPL into AI.

The empirical analysis's Figure 2 shows the NPL's foundation, where inspiration sources are noted. According to Ladanyi et al. (2020), computer science, human language, and artificial intelligence all contribute to NPL. With this knowledge, NPL is the target of process enhancement to bring it closer to human processing capabilities. Furthermore, it is clear from the debate and review of previous research that sentiment analysis can improve NPL and communication as well as comprehension of computer models.

It was discovered that there are a few key elements connected to the implications of non-performing loans (NPLs) by examining previous research on the subject. According to Öztürk et al. (2020), one of the main variables influencing the implications of NPL is cost-effectiveness. With the implication of NPL, there is also a situation of uncertainty and use cases. As a result, applying AI to enhance communication is difficult. Conversely, Kang et al. (2020) contended that as NPL use cases are evolving quickly, ROI is not a significant concern when putting NPL into practice. As can be seen, one of the main problems with the current cost-effectiveness is that it may be fixed by using NPL in large quantities.

The consequence of NPL can be interpreted in a detached way, per the Situated Theory. According to Eang & Na (2020), the situated theory of language argues that language cannot be fully understood if the context in which it is used is ignored. Thus, language can be interpreted subjectively depending on the circumstances.

The aforementioned image is linked to the situated theory of language, which displays many linguistic methods and contexts. As can be observed, social connection, authenticity of context, and constructive learning strategies all influence how subjective language is. Accordingly, the NPL model can be enhanced based on the components to comprehend the language's holistic meaning Hengst & Sherrill (2021). Furthermore, integrating NPL models with sentiment analysis models to improve communication and understandability is suggested by applying the situated theory of language to understand NPL.

 It was seen in the previous examination that every piece of literature had discussed the dangers and potential consequences of non-performing loans. Nevertheless, it was not possible to locate succinct literature discussing the fields of implication and the personal thread of data. As to the findings of Xie, Qin, and Juang (2021), models that are capable of interacting with an individual can keep that individual's personal information. As a result, these AI model characteristics create a barrier to shifting user viewpoints. As a result, gaps in the literature pertaining to security and data storage were identified. Furthermore, user engagement and perspective were not taken into consideration when discussing such matters.

An empirical analysis methodology looks at many strategies used to achieve the goals while delivering results. As a result, the application of NPL to improve understanding and communication was examined using a "Theoretical discussion" technique (Eang & Na-Songkhla, 2020). Additionally, the primary goal of the empirical study is to compare the instructional methodologies. The paper presents a comparative investigation of many elements related to the implications of NPL for understanding and communication. Discussions on "The Situated Theory of Language" were utilized to examine the potential impact of NPL on comprehension and communication. Additionally, novel research is provided through a condensed examination of multiple components, such as models, technological development, and the widespread implications of NPL models.

Theme 1: The use of sentimental analysis in the amalgamation of AI can improve communication and understanding

According to the contextual theory of language, it makes sense that knowing the context is crucial to enhancing the implications of AI. According to Holler & Levinson (2019), sentiment analysis is a tool that artificial intelligence systems can use to determine the attitudes and feelings expressed in a text. Thus, sentimental analysis can be incorporated into the NPL models to improve their interactive character. Furthermore, pleasant sentiments might inspire affirmative and uplifting responses, whilst negative ideas may elicit empathetic and supporting ones. It is clear from the understanding that sentimental analysis can lead to a more interesting and convenient experience.

The situated theory of language also states that a language can have contextual meaning. Furthermore, having tailored answers is crucial for creating an interactive conversational medium. Guo and colleagues (2020) believe that NPL can be utilized to improve the customer experience. Furthermore, knowing user attitudes enables more efficient communication by tailoring services to user needs and expectations. The NPL models must incorporate sentimental analysis for such successful communication. Thus, it makes sense that using AI and sentiment analysis together can improve understanding and communication.

Theme 2: Including ethical considerations for NPL can enhance human communication and understanding of NPL

The integration of ethical concerns into natural language processing (NLP) not only guarantees ethical AI development but also serves as a catalyst for enhancing human communication and NLP comprehension. The business of an NPL model depends on the training model for the NPL models, per the research findings of Zhao et al. (2021). Consequently, it is feasible to produce a trustworthy NPL model that incorporates ethical issues with a clear and succinct dataset. Moreover, the moral integration of fairness principles within NLP is crucial for eliminating prejudice and ensuring equal outcomes. Therefore, it is possible to have a succinct conversation based on ethical knowledge. According to Corcoran & Cecchi (2020), utilizing trustworthy models to create a trustworthy and morally sound model for users can increase transparency in the NPL model. Natural language processing (NLP) solutions that are transparent help users feel more confident because they demystify technology and make the underlying workings understandable. Thus, it can be said that considering ethical considerations can help to increase human communication and understanding of NPL.

Theme 3: Challenges such as cost and usability can hinder the implication of NPL for enhancing communication and understanding

It has been seen from earlier model analyses that there are many difficulties involved with using NPL models. Zhang et al. (2020) believe that the main concern when applying NPL models to improve communication and comprehension is cost-effectiveness. As a result, the model's mass implication offers a potential remedy for these problems. It was mentioned that infill is still in the planning stages. As a result, difficulties and glitches exist. These issues and problems can be brought to light through widespread use, and technology solutions can be employed to address them. Furthermore, it may be said that using these models from a broader angle can lower their cost.

Furthermore, according to Medina, Papakyriakopoulos, and Hegelich (2020), a significant risk associated with using such AI models is data security. Thus, attaining data security is a significant obstacle to preventing in order to improve understanding and communication. A supplementary issue that each model has to deal with is data security. NPL models, on the other hand, particularly point out difficulties like introducing a person and obtaining their personal information. Therefore, it is imperative to overcome such risks in order to achieve widespread implication.

Theme 4: Changing user acceptance and perspective aid to improve the implication of NPL for improving language processing

 User perspectives are crucial for the widespread use of NPL models and their implementation in many daily tasks. Moreover, broad user acceptance is crucial for comprehending the NPL model, which is founded on the situated theory of language. As per Cambria et al. (2020), creating a system that is easy to use and intuitive can prove advantageous in enhancing user interaction and advancing mass implication. Furthermore, it is imperative to build systems that facilitate pleasant user interaction. Therefore, it is feasible to aim for a mass impact based on the positive user interaction manipulation approach.

Furthermore, it is imperative to build systems that facilitate pleasant user interaction. Therefore, it is feasible to aim for a mass impact based on the positive user interaction manipulation approach. Enhancing the user experience can lead to the development of a better system. Additionally, Galassi, Lippi, and Torroni (2020) believe that education and awareness campaigns have a significant role in helping to alter consumer attitudes.Knowledge about non-performing loans (NPLs) in the US can be disseminated through this kind of integration.

A theoretical examination related to applying the NPL model to improve model comprehension and communication is provided. Additionally, the same factors are examined and explored through the analysis. For example, it was discovered that sentimental analysis was crucial to creating a succinct NPL model for mass implication. Furthermore, Guo et al. (2020) pointed out that sentimental analysis can offer a subjective interpretation of the NPL model. The situated theory of language postulated that an age for NPP models could be obtained by appreciating the subjective character of language. It was discovered that emotive analysis can offer a contextual concept that can be transformed into an answer for non-performing loan models. Furthermore, certain difficulties were identified during the previous literature review. Given the difficulties, it was observed that one of the main obstacles to the provision of high-quality communication and comprehension is the cost-effectiveness of NPL (Zhao et al. 2021). It was also mentioned how the proper application of the NPL model can boost user engagement and facilitate the widespread application of NPL models. So, in order to improve comprehension and communication, a thorough and cogent analysis of the potential of NPL is provided. Furthermore, the study discusses concrete remedies for the same.

In order to explore the implications of natural language processing for improving understanding and communication, theoretical analysis is done. It was discovered that sentiment analysis can be used to enhance natural language processing. Furthermore, technology improvements can be used to mitigate the risks associated with non-performing loans (NPLs). Furthermore, it was mentioned that considering ethical concerns can enhance human communication and understanding of NPL. The NPL models receive a sense of accountability from ethical issues. Building mechanisms that encourage positive user involvement is also crucial. As a result, it is possible to aim for an accumulation consequence dependent on how well users interact to change their perspectives. Improvements to the user experience may result in the creation of an improved system. Furthermore, the study's conclusions can be drawn by recommending the application of emotional analysis and widespread use of NPL models to improve communication usability.