An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys

• URL: http://arxiv.org/abs/2307.11771v2
• Date: Wed, 2 Aug 2023 00:33:22 GMT
• Title: An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys
• Authors: Edson B. Pinto-Luque
• Abstract summary: The research project aims to apply an integrated approach to natural language processing NLP to satisfaction surveys. It will focus on understanding and extracting relevant information from survey responses, analyzing feelings, and identifying recurring word patterns.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The research project aims to apply an integrated approach to natural language processing NLP to satisfaction surveys. It will focus on understanding and extracting relevant information from survey responses, analyzing feelings, and identifying recurring word patterns. NLP techniques will be used to determine emotional polarity, classify responses into positive, negative, or neutral categories, and use opinion mining to highlight participants opinions. This approach will help identify the most relevant aspects for participants and understand their opinions in relation to those specific aspects. A key component of the research project will be the analysis of word patterns in satisfaction survey responses using NPL. This analysis will provide a deeper understanding of feelings, opinions, and themes and trends present in respondents responses. The results obtained from this approach can be used to identify areas for improvement, understand respondents preferences, and make strategic decisions based on analysis to improve respondent satisfaction.

Related papers

• Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis [7.373480417322289]
  This paper addresses the emerging challenges introduced by the generative paradigm. We highlight the intricacies involved in aligning generative outputs with other evaluative metrics. Our contribution lies in paving the path for a comprehensive guideline tailored for ABSA evaluations in this generative paradigm.
  arXiv  Detail & Related papers  (2024-04-17T16:33:22Z)
• K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning [83.19215082550163]
  We propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q&A forum.
  arXiv  Detail & Related papers  (2023-12-16T08:10:10Z)
• Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs [58.620269228776294]
  We propose a task-agnostic framework for resolving ambiguity by asking users clarifying questions. We evaluate systems across three NLP applications: question answering, machine translation and natural language inference. We find that intent-sim is robust, demonstrating improvements across a wide range of NLP tasks and LMs.
  arXiv  Detail & Related papers  (2023-11-16T00:18:50Z)
• SOUL: Towards Sentiment and Opinion Understanding of Language [96.74878032417054]
  We propose a new task called Sentiment and Opinion Understanding of Language (SOUL) SOUL aims to evaluate sentiment understanding through two subtasks: Review (RC) and Justification Generation (JG)
  arXiv  Detail & Related papers  (2023-10-27T06:48:48Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• FeedbackMap: a tool for making sense of open-ended survey responses [1.0660480034605242]
  This demo introduces FeedbackMap, a web-based tool that uses natural language processing techniques to facilitate the analysis of open-ended survey responses. We discuss the importance of examining survey results from multiple perspectives and the potential biases introduced by summarization methods.
  arXiv  Detail & Related papers  (2023-06-26T23:38:24Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis [4.040584701067227]
  Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities.
  arXiv  Detail & Related papers  (2023-01-20T23:38:58Z)
• Sentiment analysis and opinion mining on E-commerce site [0.0]
  The goal of this study is to solve the sentiment polarity classification challenges in sentiment analysis. A broad technique for categorizing sentiment opposition is presented, along with comprehensive process explanations.
  arXiv  Detail & Related papers  (2022-11-28T16:43:33Z)
• A Revised Generative Evaluation of Visual Dialogue [80.17353102854405]
  We propose a revised evaluation scheme for the VisDial dataset. We measure consensus between answers generated by the model and a set of relevant answers. We release these sets and code for the revised evaluation scheme as DenseVisDial.
  arXiv  Detail & Related papers  (2020-04-20T13:26:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.