SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality

• URL: http://arxiv.org/abs/2008.08919v1
• Date: Wed, 19 Aug 2020 14:30:00 GMT
• Title: SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality
• Authors: Wissam Maamar Kouadri, Salima Benbernou, Mourad Ouziri, Themis Palpanas, Iheb Ben Amor
• Abstract summary: SentiQ is an unsupervised Markov logic Network-based approach that injects the semantic dimension in the tools through rules. Preliminary experimental results demonstrate the usefulness of SentiQ.
• Score: 13.450001922002478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The opinion expressed in various Web sites and social-media is an essential contributor to the decision making process of several organizations. Existing sentiment analysis tools aim to extract the polarity (i.e., positive, negative, neutral) from these opinionated contents. Despite the advance of the research in the field, sentiment analysis tools give \textit{inconsistent} polarities, which is harmful to business decisions. In this paper, we propose SentiQ, an unsupervised Markov logic Network-based approach that injects the semantic dimension in the tools through rules. It allows to detect and solve inconsistencies and then improves the overall accuracy of the tools. Preliminary experimental results demonstrate the usefulness of SentiQ.

Related papers

• You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools [74.98850427240464]
  We show that sentiment analysis tools disagree on the same dataset. We show that the sentiment tool used for sentiment annotation can even be predicted from its outcome.
  arXiv  Detail & Related papers  (2024-10-18T17:27:38Z)
• Effective Black Box Testing of Sentiment Analysis Classification Networks [0.0]
  Transformer-based neural networks have demonstrated remarkable performance in natural language processing tasks such as sentiment analysis. This paper presents a collection of coverage criteria specifically designed to assess test suites created for transformer-based sentiment analysis networks.
  arXiv  Detail & Related papers  (2024-07-30T14:58:11Z)
• How are Prompts Different in Terms of Sensitivity? [50.67313477651395]
  We present a comprehensive prompt analysis based on the sensitivity of a function. We use gradient-based saliency scores to empirically demonstrate how different prompts affect the relevance of input tokens to the output. We introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding.
  arXiv  Detail & Related papers  (2023-11-13T10:52:01Z)
• Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis [6.596002578395151]
  ChatGPT is a new product of OpenAI and has emerged as the most popular AI product. This study explores the use of ChatGPT as a tool for data labeling for different sentiment analysis tasks.
  arXiv  Detail & Related papers  (2023-06-18T12:20:42Z)
• 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)
• Causal Intervention Improves Implicit Sentiment Analysis [67.43379729099121]
  We propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV) We first review sentiment analysis from a causal perspective and analyze the confounders existing in this task. Then, we introduce an instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment.
  arXiv  Detail & Related papers  (2022-08-19T13:17:57Z)
• Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa [23.00810941211685]
  Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis. Recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem.
  arXiv  Detail & Related papers  (2022-07-06T03:50:31Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
  We establish a novel set of evaluation criteria for such feature based explanations by analysis. We obtain new explanations that are loosely necessary and sufficient for a prediction. We extend the explanation to extract the set of features that would move the current prediction to a target class.
  arXiv  Detail & Related papers  (2020-05-31T05:52:05Z)
• Survey on Visual Sentiment Analysis [87.20223213370004]
  This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
  arXiv  Detail & Related papers  (2020-04-24T10:15:22Z)

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.