Machine Learning-Based Sentiment Analysis for Uncovering Pain Points in Online Reviews

EasyChair Preprint no. 13775

Abstract

Sentiment analysis is a valuable technique for understanding customer opinions and sentiments expressed in online reviews. Uncovering pain points, which are negative aspects or concerns raised by customers, is crucial for businesses to improve their products and services. This abstract focuses on machine learning-based sentiment analysis, which leverages the power of algorithms to automatically classify sentiments in large volumes of textual data.

The abstract outlines the key components of the approach. It begins by introducing the concept of sentiment analysis and its significance in the context of online reviews. Next, it emphasizes the use of machine learning algorithms, which offer advantages such as scalability, adaptability, and accuracy in sentiment analysis tasks.

The abstract then highlights the data collection and preparation phase, which involves gathering relevant online review data and preprocessing it to ensure high-quality input for the sentiment analysis model. It further describes the process of building a sentiment analysis model, including the selection of appropriate algorithms and feature engineering techniques.

The abstract delves into the specific application of sentiment analysis for uncovering pain points in online reviews. It discusses the analysis of sentiment scores and patterns to identify pain points, as well as techniques for extracting pain point keywords or phrases. Visualizations such as word clouds and sentiment heatmaps are mentioned as effective tools for understanding pain points and sentiment distribution.

Keyphrases: Customer Dissatisfaction, Customer Experiences, customer feedback, Pain Points, Process Improvement, Product functionality, Root Causes

@Booklet{EasyChair:13775,
  author = {Edwin Frank and Samon Daniel},
  title = {Machine Learning-Based Sentiment Analysis for Uncovering Pain Points in Online Reviews},
  howpublished = {EasyChair Preprint no. 13775},

  year = {EasyChair, 2024}}