Journal ArticleDOI
Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data
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TLDR
A hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains for sentiment analysis of consumer reviews posted on social media.Abstract:
Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.read more
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TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.
Md. Shahriare Satu,Md. Imran Khan,Mufti Mahmud,Shahadat Uddin,Matthew A. Summers,Julian M.W. Quinn,Mohammad Ali Moni,Mohammad Ali Moni +7 more
TL;DR: In this paper, the authors designed an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy.
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An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification
TL;DR: New cognitive computing with the big data analysis tool for Sentiment Analysis is presented and improved classification performance of the proposed BBSO-FCM model is highlighted in terms of different measures.
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An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification
TL;DR: In this paper , Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets, which is useful for businesses to take commercial benefits insight from textoriented content.
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Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis
TL;DR: In this article , the authors proposed a bidirectional Encoder Representation from Transformers (BERT) based model which applies ensemble learning methods with a text sentiment analysis to identify harmful news, aiming to provide readers with a way to identify news content so as to help them to judge whether the information provided is in a more neutral manner.
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Bengali text document categorization based on very deep convolution neural network
TL;DR: The proposed intelligent text classification model comprises GloVe embedding and Very Deep Convolution Neural Network (VDCNN) classifier, and the Embedding Parameters Identification (EPI) Algorithm, which selects the best embedding parameters for low-resource languages (including Bengali).
References
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Long short-term memory
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Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
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Journal Article
Natural Language Processing (Almost) from Scratch
TL;DR: A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Proceedings ArticleDOI
Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis
TL;DR: A new approach to phrase-level sentiment analysis is presented that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions.