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Open Access Journal Article

Text Mining for Customer Sentiment Analysis

by Olivia Smith 1
1
Olivia Smith
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Author to whom correspondence should be addressed.
MAD  2023 5(2):45; https://doi.org/10.69610/j.mad.20231130
Received: 22 September 2023 / Accepted: 20 October 2023 / Published Online: 30 November 2023
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Abstract

The rapid advancements in information technology have led to an exponential increase in the volume of textual data generated by customers across various platforms. This surge in unstructured data presents a significant challenge for businesses seeking to gain insights into customer sentiment and preferences. This paper delves into the field of text mining and its application in customer sentiment analysis. It outlines the importance of sentiment analysis in understanding the emotional tone and opinions of customers, which is crucial for informed decision-making and strategic planning. The paper explores the various methodologies employed in text mining for sentiment analysis, including natural language processing (NLP), machine learning algorithms, and sentiment lexicons. Additionally, it discusses the challenges faced in the field, such as handling negations, sarcasm, and context-awareness. Furthermore, the paper evaluates the performance of different text mining techniques and proposes a framework for an effective sentiment analysis solution. The findings reveal that text mining is not only essential but also evolving rapidly, providing businesses with valuable tools to analyze customer sentiment and improve their products and services.

Keywords: Text Mining; Customer Sentiment Analysis; Natural Language Processing (NLP); Machine Learning; Sentiment Lexicons.;
Copyright: © 2023 by Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Smith, O. (2023). Text Mining for Customer Sentiment Analysis. Management Analytics and Decision, 5(2), 45. doi:10.69610/j.mad.20231130
ACS Style
Smith, O. Text Mining for Customer Sentiment Analysis. Management Analytics and Decision, 2023, 5, 45. doi:10.69610/j.mad.20231130
AMA Style
Smith O. Text Mining for Customer Sentiment Analysis. Management Analytics and Decision; 2023, 5(2):45. doi:10.69610/j.mad.20231130
Chicago/Turabian Style
Smith, Olivia 2023. "Text Mining for Customer Sentiment Analysis" Management Analytics and Decision 5, no.2:45. doi:10.69610/j.mad.20231130

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ACS Style
Smith, O. Text Mining for Customer Sentiment Analysis. Management Analytics and Decision, 2023, 5, 45. doi:10.69610/j.mad.20231130
AMA Style
Smith O. Text Mining for Customer Sentiment Analysis. Management Analytics and Decision; 2023, 5(2):45. doi:10.69610/j.mad.20231130
Chicago/Turabian Style
Smith, Olivia 2023. "Text Mining for Customer Sentiment Analysis" Management Analytics and Decision 5, no.2:45. doi:10.69610/j.mad.20231130
APA style
Smith, O. (2023). Text Mining for Customer Sentiment Analysis. Management Analytics and Decision, 5(2), 45. doi:10.69610/j.mad.20231130

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