A Deep Neural Framework for Contextual Affect Detection

• URL: http://arxiv.org/abs/2001.10169v1
• Date: Tue, 28 Jan 2020 05:03:15 GMT
• Title: A Deep Neural Framework for Contextual Affect Detection
• Authors: Kumar Shikhar Deep, Asif Ekbal, Pushpak Bhattacharyya
• Abstract summary: A short and simple text carrying no emotion can represent some strong emotions when reading along with its context. We propose a Contextual Affect Detection framework which learns the inter-dependence of words in a sentence.
• Score: 51.378225388679425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A short and simple text carrying no emotion can represent some strong emotions when reading along with its context, i.e., the same sentence can express extreme anger as well as happiness depending on its context. In this paper, we propose a Contextual Affect Detection (CAD) framework which learns the inter-dependence of words in a sentence, and at the same time the inter-dependence of sentences in a dialogue. Our proposed CAD framework is based on a Gated Recurrent Unit (GRU), which is further assisted by contextual word embeddings and other diverse hand-crafted feature sets. Evaluation and analysis suggest that our model outperforms the state-of-the-art methods by 5.49% and 9.14% on Friends and EmotionPush dataset, respectively.

Related papers

• Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue [67.09698638709065]
  We propose a novel sEntiment-enhanceD Graph-based multimodal sarcasm Explanation framework, named EDGE. In particular, we first propose a lexicon-guided utterance sentiment inference module, where a utterance sentiment refinement strategy is devised. We then develop a module named Joint Cross Attention-based Sentiment Inference (JCA-SI) by extending the multimodal sentiment analysis model JCA to derive the joint sentiment label for each video-audio clip.
  arXiv  Detail & Related papers  (2024-02-06T03:14:46Z)
• ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models [92.60282074937305]
  We introduce ConTextual, a novel dataset featuring human-crafted instructions that require context-sensitive reasoning for text-rich images. We conduct experiments to assess the performance of 14 foundation models and establish a human performance baseline. We observe a significant performance gap of 30.8% between GPT-4V and human performance.
  arXiv  Detail & Related papers  (2024-01-24T09:07:11Z)
• USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset [0.0]
  This paper proposes enhancing performance by leveraging the Mutual Reinforcement Effect(MRE) between individual words and the overall text. To support our research, we annotated four novel Sentiment Text Classification and Part of Speech(SCPOS) datasets. Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a 7-billion parameter size.
  arXiv  Detail & Related papers  (2023-09-07T15:35:00Z)
• DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis [84.80347062834517]
  We introduce DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction.
  arXiv  Detail & Related papers  (2022-11-10T17:18:20Z)
• CEFER: A Four Facets Framework based on Context and Emotion embedded features for Implicit and Explicit Emotion Recognition [2.5137859989323537]
  We propose a framework that analyses text at both the sentence and word levels. We name it CEFER (Context and Emotion embedded Framework for Emotion Recognition) CEFER combines the emotional vector of each word, including explicit and implicit emotions, with the feature vector of each word based on context.
  arXiv  Detail & Related papers  (2022-09-28T11:16:32Z)
• Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis [70.98130990040228]
  We propose a Context-based Hierarchical Attention Capsule(Chat-Capsule) model, which models both utterance-level and dialog-level emotions and their interrelations. On a dialog dataset collected from customer support of an e-commerce platform, our model is also able to predict user satisfaction and emotion curve category.
  arXiv  Detail & Related papers  (2022-03-23T08:04:30Z)
• Detecting Emotion Carriers by Combining Acoustic and Lexical Representations [7.225325393598648]
  We focus on Emotion Carriers (EC) defined as the segments that best explain the emotional state of the narrator. EC can provide a richer representation of the user state to improve natural language understanding. We leverage word-based acoustic and textual embeddings as well as early and late fusion techniques for the detection of ECs in spoken narratives.
  arXiv  Detail & Related papers  (2021-12-13T12:39:53Z)
• TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora [14.844685568451833]
  We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface.
  arXiv  Detail & Related papers  (2021-03-19T21:26:28Z)
• BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis [18.1320976106637]
  The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. We propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis.
  arXiv  Detail & Related papers  (2020-05-31T11:13:13Z)
• A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text [68.8204255655161]
  This work introduces a new method to consider subjectivity and general context dependency in text analysis. By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
  arXiv  Detail & Related papers  (2020-05-12T21:26:04Z)

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.