Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an F-measure of 88.7% considering the complete dataset.

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Convolutional Neural Network for Customer’s Opinion on Amazon Products

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5670.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6634-6643 ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sentiment Analysis ◽

Latent Dirichlet Allocation ◽

Opinion Mining ◽

Text Documents ◽

Customer Churn ◽

Learning Classifier ◽

Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

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A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2021.13.2.13 ◽

2021 ◽

Vol 13 (2) ◽

pp. 465-476

Author(s):

Siji George C. G. ◽

◽

B Sumathi ◽

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Random Forest Classifier ◽

Sentiment Classification ◽

Learning Approach ◽

Fine Grained

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Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

Remote Sensing ◽

10.3390/rs13193859 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3859

Author(s):

Joby M. Prince Czarnecki ◽

Sathishkumar Samiappan ◽

Meilun Zhou ◽

Cary Daniel McCraine ◽

Louis L. Wasson

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Quality ◽

Convolutional Neural Network ◽

Precision Agriculture ◽

Edge Computing ◽

Training Data ◽

Learning Approaches ◽

Sky Conditions

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

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Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8856972 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marek Wodzinski ◽

Andrzej Skalski ◽

Daria Hemmerling ◽

Juan Rafael Orozco-Arroyave ◽

Elmar Noth

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Disease Detection ◽

Learning Approach

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Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach

Surgical Endoscopy ◽

10.1007/s00464-019-07281-0 ◽

2019 ◽

Vol 34 (11) ◽

pp. 4924-4931 ◽

Cited By ~ 10

Author(s):

Daichi Kitaguchi ◽

Nobuyoshi Takeshita ◽

Hiroki Matsuzaki ◽

Hiroaki Takano ◽

Yohei Owada ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real Time ◽

Learning Approach ◽

Laparoscopic Sigmoidectomy ◽

Phase Recognition

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Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach

Procedia Computer Science ◽

10.1016/j.procs.2018.05.069 ◽

2018 ◽

Vol 132 ◽

pp. 679-688 ◽

Cited By ~ 39

Author(s):

Sakshi Indolia ◽

Anil Kumar Goswami ◽

S.P. Mishra ◽

Pooja Asopa

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Conceptual Understanding ◽

Learning Approach

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Twitter Sentiment Analysis Model

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36309 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 296-302

Author(s):

Pushkar Dubey

Keyword(s):

Machine Learning ◽

Social Networks ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Learning Approach ◽

Analysis Model ◽

Market Place ◽

The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

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