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Sentiment Analysis Using Deep Learning Techniques: A Review

Qurat Tul, Mubashir Ali, Amna Riaz, Amna Noureen, Muhammad Kamranz, Babar Hayat, A. Rehman
2017 International Journal of Advanced Computer Science and Applications  
This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems  ...  , brands, and politics.  ...  (DANN) Twitter brand sentiment analysis (for justinbieber brand) Total 10,345,184 tweets related to justinbieber brand TABLE VII .  ... 
doi:10.14569/ijacsa.2017.080657 fatcat:us4hwclsx5ghtjo4v5vkvfkqqm

Sentiment Analysis using Artificial Neural Network

2020 International journal of recent technology and engineering  
The paper presents a survey with main focus on performance of different artificial neural networks used for opinion mining or sentiment analysis while it also includes various machine learning approaches  ...  Analysis of public sentiments deals with the determination of the polarity of different public opinions or reviews into either the category of positive, negative or neutral.  ...  Then, the targeted approach of twitter sentiment analysis was further enhanced by Ghiassi et al. [13] in 2016 itself that used supervised feature engineering and artificial neural network.  ... 
doi:10.35940/ijrte.e6450.018520 fatcat:vyee4yzojje3bf7cm3zesm3k7i

Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

M. Ghiassi, J. Skinner, D. Zimbra
2013 Expert systems with applications  
In this research, we introduce an approach to supervised feature reduction using n-grams and statistical analysis to develop a Twitter-specific lexicon for sentiment analysis.  ...  We augment this reduced Twitter-specific lexicon with brand-specific terms for brand-related tweets.  ...  The use of a highly explanatory Twitter-specific lexicon in this research demonstrated that it offered capabilities for the Justin Bieber brand to recognize emerging issues with their brand identity.  ... 
doi:10.1016/j.eswa.2013.05.057 fatcat:wjodhhyps5givfbpmbr6e6bwca

Neural Network Based Context Sensitive Sentiment Analysis

S. Suruthi, M. Pradeeba, A. Sumaiya, J.I Sheeba
2015 International Journal of Computer Applications Technology and Research  
This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or  ...  The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform.  ...  Twitter brand sentiment analysis: A hybrid system using ngram analysis and dynamic artificial neural network makes several contributions to twitter sentiment analysis, demonstrated through application  ... 
doi:10.7753/ijcatr0403.1004 fatcat:ozmn7oxktrghlgic4mgqb4pnga

STOCK MARKET PREDICTION USING MACHINE LEARNING METHODS

SUBHADRA KOMPELLA, KALYANA CHAKRAVARTHY CHILUKURI
2019 INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY  
With the help of sentiment analysis, we found the polarity score of the new article and that helped in forecasting accurate result.  ...  Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as  ...  The Nearest Neighbor and the Neural Networks Practices have been used for forecasting of the market.  ... 
doi:10.34218/ijcet.10.3.2019.003 fatcat:w5lwxdnoebagxarkhkl4t4qqhe

Systematic reviews in sentiment analysis: a tertiary study

Alexander Ligthart, Cagatay Catal, Bedir Tekinerdogan
2021 Artificial Intelligence Review  
Different features, algorithms, and datasets used in sentiment analysis models are mapped.  ...  According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.  ...  Lexicons are used for defining domain-related features that are used as input for a machine learning classifier.  ... 
doi:10.1007/s10462-021-09973-3 fatcat:zo7igc4fnnh47kyafncbfmaf3u

A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis

Faiza Mehmood, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Rehab Shahzadi, Waqar Mahmood, Muhammad Nabeel Asim
2020 IEEE Access  
STATE-OF-THE-ART MACHINE AND DEEP LEARNING WORK FOR ROMAN URDU SENTIMENT ANALYSIS Sentiment analysis is the core building block behind the development of more appealing marketing and branding strategies  ...  Afterward, considering the effectiveness of Recurrent Neural Network (RNN) for regulating the flow of information and Convolutional Neural Network (CNN) for the acquisition of most discriminative features  ...  For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ developed experimental dataset will be publicly available.  ... 
doi:10.1109/access.2020.3030885 fatcat:74xtllokurhfjdzpjg2ispgaxq

A Novel Adaptable Approach for Sentiment Analysis

Aishwarya R, Ashwatha C, Deepthi A, Beschi Raja J
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
And also about the ada boosting algorithm and artificial neural networks by which the optimized prediction accuracy is achieved.  ...  Sentiment analysis is the process of mining the sentiments from the data that are available in online platforms and categorizing the opinion towards a particular entity that falls on three different categories  ...  Based on many of the statistics of the Twitter growth rate, it's provident to use Twitter as the data source for the sentimental analysis.  ... 
doi:10.32628/cseit195263 fatcat:ph3ezyuawnc33o5tyqeyv2ldvy

IMAGE SENTIMENTAL ANALYSIS: AN OVERVIEW

Vedashree C.R and Sowmyashree S. V. Sunil Kumar
2022 Zenodo  
On social networking sites, images are the simplest way for people to communicate their emotions.  ...  This paper introduces the area of Image Sentiment Analysis and examines the issues that it raises.  ...  For textual sentiment analysis, they use a CNN built on top of pre-trained word vectors, and for visual sentiment analysis, they use a deep convolution neural network (DNN) with generalized dropout.  ... 
doi:10.5281/zenodo.6507385 fatcat:t4ppdghzlvg2neswfsfovbvoai

Intelligent Control Systems in Urban Planning Conflicts: Social Media Users' Perception

Nailia Gabdrakhmanova, Maria Pilgun
2021 Applied Sciences  
Artificial neural networks, differential equations, and mathematical statistics were involved in building the models.  ...  The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users' perceptions based on  ...  Sentiment analysis was performed using the Eureka Engine sentiment determination module.  ... 
doi:10.3390/app11146579 fatcat:thuiwzt2lbhlhh634ba23d3mj4

Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

Sangeeta Rani, Nasib Singh, Preeti Gulia
2021 International Journal of Advanced Computer Science and Applications  
This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis.  ...  Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis.  ...  [3] proposed twitter sentiment analysis by using POS specific polarity features and explored tree kernels to prevent the need for tedious feature engineering. 11,875 manually labeled tweets publically  ... 
doi:10.14569/ijacsa.2021.0120430 fatcat:kccjxxwia5gmfpwixmemza2oqm

Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers

Kostadin Mishev, Ana Gjorgjevikj, Irena Vodenska, Lubomir T. Chitkushev, Dimitar Trajanov
2020 IEEE Access  
We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers.  ...  Sentiment analysis models can provide an efficient method for extracting actionable signals from the news.  ...  Additionally, paragraph vectors can be used as features for the paragraph, which can be fed as input to a classifier or to a neural network, making them appropriate for evaluation of sentiment analysis  ... 
doi:10.1109/access.2020.3009626 fatcat:efvchm5chbgibfzuhw7zjmh7ii

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

2020 International journal of recent technology and engineering  
This paper provides a platform for analyzing and summarizing the sentiments expressed by users or customers in the field of online tourism.  ...  The proposed system filters tourism online reviews and classifies them using sentimental technique with the help of deep learning technique.  ...  authors mentioned in this paper gives results that implementation of sentiment analysis in the field of tourism using latest neural network based technique called deep learning will be highly efficient  ... 
doi:10.35940/ijrte.f7390.038620 fatcat:fsgxewmn2jdr3linqptws4yd7y

Predictive Analysis on Twitter: Techniques and Applications [chapter]

Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar
2018 Lecture Notes in Social Networks  
Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking  ...  Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics  ...  The multi-entry neural network architecture (MENET) developed for location prediction uses words, the semantics of the paragraph (using doc2vec [79] ), network features and topology (using node2vec [  ... 
doi:10.1007/978-3-319-94105-9_4 fatcat:knquzcuqcjdjjguzq435nq5kni

Predictive Analysis on Twitter: Techniques and Applications [article]

Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar
2018 arXiv   pre-print
Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking  ...  Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics  ...  The multi-entry neural network architecture (MENET) developed for location prediction uses words, the semantics of the paragraph (using doc2vec [79] ), network features and topology (using node2vec [  ... 
arXiv:1806.02377v1 fatcat:gm5cqpmgvfggzgxgzocv4c3fqi
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