Text Mining Based Approach to Customer Sentiment Analysis Using Machine Learning

2018 ◽  
Vol 15 (6) ◽  
pp. 58-65
Author(s):  
Gurjeet Kaur
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Sentiment Analysis

scholarly journals Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

2021 ◽  
pp. 118-124
Author(s):  
Md. Serajus Salekin Khan ◽  
Sanjida Reza Rafa ◽  
Al Ekram Hossain Abir ◽  
Amit Kumar Das
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Computer Science ◽  
Sentiment Analysis ◽  
Research Area ◽  
Science Study ◽  
Machine Learning Technique ◽  
Mining Technique ◽  
Svm Algorithm ◽  
Learning Technique

In this present era, sentiment analysis is considered as one of the most rapidly growing fields of computer science study. It is a text mining technique which is automated and determines the emotion of a text. A text can be divided into many emotions using sentiment analysis. Since there are some studies on emotion analysis in the Bangla language, it is regarded as a key research area in the field of analyzing Bangla language. This paper works with five different emotions and those are Happy, Sad, Angry, Surprise and Excited. Apart from these emotions our paper also deals with two categories, such as Abusive and Religious. We proposed a method of machine learning technique which is the SVM algorithm to extract these five individual emotions from Bangla text.

scholarly journals The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Triyas Hevianto Saputro ◽  
Arief Hermawan
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Classification Model ◽  
Training Process ◽  
Accuracy Improvement ◽  
Spelling Correction ◽  
Preprocessing Technique ◽  
Selection Of

Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model.

scholarly journals Sentimental Analysis and Deep Learning : A Survey

2020 ◽  
pp. 212-220
Author(s):  
Princy Baby ◽  
Krishnapriya B
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Deep Structure ◽  
Structured Data ◽  
Deep Belief Networks ◽  
Analysis Techniques ◽  
Main Target

Sentiment Analysis is an ongoing field of research in text mining. Sentiment Analysis is the computational treatment of opinions, Sentiments, and subjectivity of text. Many recently proposed algorithms enhancements and various Sentiment Analysis applications are investigated and presented briefly in this survey. The related fields to Sentiment Analysis that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Sentiment Analysis techniques and the related fields with brief details. In recent years machine learning has received greater attention with the success of deep learning. Deep learning can create deep models of complex multivariate structures in structured data. Though deep learning can be characterized in several different ways, the most important is that deep learning can learn higher-order interactions among features using a cascade of many layers. Deep learning has been applied to neural networks and across many fields, with significant successes in many applications. Convolution neural networks, deep belief networks, and many other approaches have been proposed to enhance the abilities of deep structure networks

scholarly journals Using text mining techniques to identify healthcare providers at risk: an exploratory study (Preprint)

2020 ◽  
Author(s):  
Iris Hendrickx ◽  
Tim Voets ◽  
Pieter van Dyk ◽  
Rudolph B Kool
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Exploratory Study ◽  
Healthcare Providers ◽  
Supervised Machine Learning ◽  
Care Providers ◽  
Severity Prediction ◽  
Dutch Health

BACKGROUND Regulatory bodies such as healthcare inspectorates can identify risks of healthcare providers by analyzing patient complaints. Text mining techniques (automatic text analysis based on machine learning), might help by identifying specific patterns and signals for risks on quality and safety issues. OBJECTIVE The aim of this study was to explore whether text mining techniques might be used to identify healthcare providers at risk. METHODS We performed an exploratory study on a complaints database of the Dutch Health and Youth Care Inspectorate with more than 22000 written complaints. We studied a range of supervised machine learning techniques to automatically determine the severity of incoming complaints. We investigated several features based on the complaints’ content, including sentiment analysis, to decide which were helpful for severity prediction. Finally, we took the list of health care providers and their organization-specific complaints to determine the average severity of complaints per organization. We performed a keyword analysis in order to give the Inspectorate insight in the patterns and severity per organization. RESULTS The data preparation and preprocessing were time-consuming one-off costs, mainly because we had to create a safe and efficient digital research environment. A straightforward text classification approach using a bag-of-words feature representation worked best for severity prediction. The usage of sentiment analysis for severity prediction was not helpful. Finally, we produced a list of n-grams of healthcare providers with the most complaints to inform the Inspectorate about the specific combination of words for these organizations. CONCLUSIONS Text mining techniques can support inspectorates with fully automatic analysis of complaints. They can give insights in patterns, detect possible blind spots, or support prioritizing follow-up supervision activities by sorting complaints on severity per organization or per sector. An appropriate data science and ICT infrastructure is crucial and indispensable for applied text mining.

Hybrid Approach to Sentiment Analysis based on Syntactic Analy- sis and Machine Learning

2010 ◽  
Vol 14 (2) ◽  
pp. 159-181
Author(s):  
MUNPYO HONG ◽  
MIYOUNG SHIN ◽  
Shinhye Park ◽  
Hyungmin Lee
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Hybrid Approach

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

POLITICAL SENTIMENT ANALYSIS ON DELHI USING MACHINE LEARNING

2020 ◽  
Vol 9 (3) ◽  
pp. 1239-1250
Author(s):  
D. Yadav ◽  
A. Sharma ◽  
S. Ahmad ◽  
U. Chandra
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis
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