Results 31 to 40 of about 265,995 (181)
Multitask Learning for Fine-Grained Twitter Sentiment Analysis [PDF]
, 2017 Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.
Amini, Massih-Reza +2 more
core +2 more sources
Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification
Applied Sciences, 2019 Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models
Wenkuan Li +5 more
doaj +1 more source
Comparative Study of Deep Learning-Based Sentiment Classification
IEEE Access, 2020 The purpose of sentiment classification is to determine whether a particular document has a positive or negative nuance. Sentiment classification is extensively used in many business domains to improve products or services by understanding the opinions ...
Seungwan Seo +4 more
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Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets
IEEE Access, 2020 Twitter sentiment analysis provides valuable feedback from public emotion concerning certain events or products. Current research has been focused on obtaining sentiment features from vectorized lexical and syntactic feature from tweets, without further ...
Wei Lun Lim +2 more
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Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification [PDF]
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2019 Accepted to BlackBoxNLP Workshop at ACL ...
Barnes, Jeremy Claude +2 more
openaire +3 more sources
IEEE Access, 2018 Sentiment analysis is an important problem in natural language processing, which plays an important role in many fields, such as information forecasting, knowledge classification, and product review. Because Tibetan microblogs have their own unique form,
Lirong Qiu, Qiao Lei, Zhen Zhang
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SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled data
Machine Learning with Applications, 2021 In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable.
Salim Sazzed, Sampath Jayarathna
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Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
, 2018 We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their ...
Iyyer, Mohit +2 more
core +1 more source
Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.
PLoS ONE, 2018 Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites.
Ahmed Al-Saffar +5 more
doaj +1 more source
Future Internet, 2019 Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit ...
Wenkuan Li +3 more
doaj +1 more source