Natural Language Processing Techniques Combined with Machine Learning for Pain Point Identification in Online Forums and Communities

EasyChair Preprint 13827

Abstract

Online forums and communities have become valuable platforms for individuals to express their opinions, seek support, and share experiences. However, the sheer volume of user-generated content makes it challenging to identify and address the pain points effectively. This paper explores the integration of natural language processing (NLP) techniques with machine learning (ML) to automate pain point identification in online forums and communities.

The NLP techniques discussed include text preprocessing, sentiment analysis, named entity recognition (NER), and topic modeling. These techniques enable the extraction of meaningful information from unstructured text data, allowing for a deeper understanding of user sentiments, key entities, and latent topics related to pain points. Additionally, ML techniques such as supervised learning, unsupervised learning, and deep learning are employed to train models that can classify, cluster, and predict pain points based on the extracted features.

The process of pain point identification involves data collection from online forums, feature engineering, model training, and evaluation. The trained models are then applied to unseen data, enabling the identification of prevalent pain points and the extraction of valuable insights and patterns. The applications and benefits of this approach include customer feedback analysis, community management, and business intelligence, empowering organizations to improve products and services, enhance customer satisfaction, and make data-driven decisions.

Keyphrases: Natural Language Processing, Predictive Analytics, Recommendation Systems, computer vision, machine learning, model evaluation, model training

@booklet{EasyChair:13827,
  author    = {Godwin Olaoye and Samon Daniel},
  title     = {Natural Language Processing Techniques Combined with Machine Learning for Pain Point Identification in Online Forums and Communities},
  howpublished = {EasyChair Preprint 13827},
  year      = {EasyChair, 2024}}