Results 51 to 60 of about 22,491,724 (165)
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors [PDF]
arXiv, 2022 Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR)
arxiv
Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis
Human Language Technology - The Baltic Perspectiv, 2005 This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions.
Theresa Wilson +2 more
semanticscholar +1 more source
General Purpose Textual Sentiment Analysis and Emotion Detection Tools [PDF]
, 2013 Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc.
Bellalem, Nadia +2 more
core +2 more sources
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification [PDF]
arXiv, 2016 Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal.
arxiv
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon [PDF]
arXiv, 2020 Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment classification. One major issue with this approach is that many sentiment words are domain dependent.
arxiv
Basic tasks of sentiment analysis
, 2017 Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment.
DM Blei +20 more
core +1 more source
Causal Intervention Improves Implicit Sentiment Analysis [PDF]
arXiv, 2022 Despite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. This may be due to the fact that they may latch onto spurious correlations ("shortcuts", e.g., focusing only on explicit sentiment words), resulting in undermining the effectiveness and robustness of the learned model. In this
arxiv
Discovering New Sentiments from the Social Web [PDF]
, 2014 A persistent challenge in Complex Systems (CS) research is the phenomenological reconstruction of systems from raw data. In order to face the problem, the use of sound features to reason on the system from data processing is a key step.
Borrego-Díaz, Joaquín +1 more
core +1 more source
Sentiment Identification in Code-Mixed Social Media Text [PDF]
arXiv, 2017 Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever
arxiv
Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis [PDF]
arXiv, 2023 Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures.
arxiv