Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts

• URL: http://arxiv.org/abs/2210.02063v1
• Date: Wed, 5 Oct 2022 07:34:07 GMT
• Title: Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts
• Authors: An Long Doan, Son T. Luu
• Abstract summary: Finding out the words that represent the emotion from the text can improve the performance of the classification models for the sentiment analysis task. Our experimental results show that the emotion lexicon combined with the classification model improves the performance of models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The sentiment analysis task has various applications in practice. In the sentiment analysis task, words and phrases that represent positive and negative emotions are important. Finding out the words that represent the emotion from the text can improve the performance of the classification models for the sentiment analysis task. In this paper, we propose a methodology that combines the emotion lexicon with the classification model for enhancing the accuracy of the models. Our experimental results show that the emotion lexicon combined with the classification model improves the performance of models.

Related papers

• Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models [1.342834401139078]
  This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral.
  arXiv  Detail & Related papers  (2024-09-19T15:31:12Z)
• Emotion and Sentiment Guided Paraphrasing [3.5027291542274366]
  We introduce a new task of fine-grained emotional paraphrasing along emotion gradients. We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels. We propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation.
  arXiv  Detail & Related papers  (2023-06-08T20:59:40Z)
• 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)
• Speech Synthesis with Mixed Emotions [77.05097999561298]
  We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
  arXiv  Detail & Related papers  (2022-08-11T15:45:58Z)
• Contrast and Generation Make BART a Good Dialogue Emotion Recognizer [38.18867570050835]
  Long-range contextual emotional relationships with speaker dependency play a crucial part in dialogue emotion recognition. We adopt supervised contrastive learning to make different emotions mutually exclusive to identify similar emotions better. We utilize an auxiliary response generation task to enhance the model's ability of handling context information.
  arXiv  Detail & Related papers  (2021-12-21T13:38:00Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability [82.39099867188547]
  Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. We propose a new interactive training paradigm for ETTS, denoted as i-ETTS. We formulate an iterative training strategy with reinforcement learning to ensure the quality of i-ETTS optimization.
  arXiv  Detail & Related papers  (2021-04-03T13:52:47Z)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)
• SpanEmo: Casting Multi-label Emotion Classification as Span-prediction [15.41237087996244]
  We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction. We introduce a loss function focused on modelling multiple co-existing emotions in the input sentence. Experiments performed on the SemEval2018 multi-label emotion data over three language sets demonstrate our method's effectiveness.
  arXiv  Detail & Related papers  (2021-01-25T12:11:04Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)

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.