Uday Kumar_Week 8 assignement

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Southern New Hampshire University *

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511

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Electrical Engineering

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Dec 6, 2023

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docx

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5

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Abstract: In today's data-driven world, text analytics and natural language processing (NLP) have emerged as potent tools for extracting meaningful insights from unstructured text data. This gives businesses a competitive advantage. This paper looks at how text analytics and natural language processing (NLP) fit into business analytics. It focuses on how important they are for understanding customer sentiment and finding hidden topics. This paper demonstrates the practical applications of sentiment analysis and topic modeling and their impact on business decision-making by providing examples of their use in a variety of industries.[ CITATION Bir09 \l 1033 ] 1.Introduction: The explosion of digital data in recent years has necessitated efficient methods for analyzing and interpreting unstructured text data. Deriving useful insights from unstructured textual information is the process of text analytics, a subset of data analytics. Regular language handling (NLP), then again, centers around the connection among PCs and human language. Through the use of topic modeling and sentiment analysis, this paper demonstrates the practical applications of NLP and text analytics in business analytics. 2. Message Examination and Opinion Investigation 2.1 Definition and Significance Text examination includes the utilization of computational strategies to remove significant data from unstructured text information. One important use of text analytics is sentiment analysis, which aims to determine whether a piece of text has a positive, negative, or neutral sentiment. Businesses can effectively comprehend and respond to customer sentiment thanks
to the useful insights that sentiment analysis provides into customer opinions, feedback, and attitudes. 2.2 Applications of Customer Sentiment Analysis Customer sentiment analysis is utilized extensively across a wide range of sectors. For instance, in the e-commerce sector, businesses can comprehend levels of customer satisfaction and identify areas for improvement by analyzing customer reviews and comments on social media. By breaking down feeling progressively, organizations can address client concerns immediately, upgrading client experience and dedication. Also, in the neighborliness business, opinion examination can assist lodgings and eateries with checking visitor fulfillment, distinguish administration related issues, and make fitting moves to further develop consumer loyalty.[ CITATION Liu12 \l 1033 ] 2.3 Models in Ventures In the medical care industry, opinion examination can be used to dissect patient criticism from overviews, online gatherings, and virtual entertainment stages. Hospitals and healthcare providers can improve patient care by recognizing patterns in patient sentiments, comprehending pain points. To keep an eye on the safety of their products and spot potential problems, pharmaceutical companies can look at how people feel about drug reviews and reports of adverse events. 3. Regular Language Handling and Point Demonstrating 3.1 Definition and Significance Regular language handling (NLP) centers around the connection among PCs and human language, empowering machines to comprehend and create human language. Because they make it possible to extract meaningful information from unstructured text data, NLP
techniques are crucial to text analytics. The goal of topic modeling, a popular NLP technique, is to find hidden topics in a corpus of documents. 3.2 Uses in Identifying Hidden Topics By using topic modeling, businesses can gain insight into massive amounts of unstructured text data. Topic modeling can be used to analyze financial reports, earnings call transcripts, and news articles in the finance industry to identify emerging trends, market sentiments, and investor opinions. Topic modeling can be utilized by social media platforms to comprehend user interests, identify hot topics, and personalize content recommendations.[ CITATION Ram10 \l 1033 ] 3.3 Industry Examples: Market research firms can use topic modeling to look at customer reviews and feedback to figure out what customers want, find new trends, and make good marketing plans. Media associations can utilize theme displaying to order news stories, empowering productive substance association and recovery. By understanding the hidden points, organizations can go with information driven choices and answer client needs more effectively. 4. Studies of Cases 4.1 Sentiment Analysis Study of Case: E-commerce Industry: A major e-commerce company looked at customer ratings and reviews using sentiment analysis. By utilizing NLP methods, they distinguished items that clients cherished and those that required improvement. Product development efforts were guided by this data, which led to increased sales and customer satisfaction.
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