scholarly journals Sentiment Analysis using Rapid Miner

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1589-1594
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Quality Information ◽  
Classification Techniques ◽  
Major Task ◽  
Text Document ◽  
Sentence Level ◽  
Day By Day

Now a day the data grows day by day so data mining replaced by big data. Under data mining, Text mining is one of the processes of deriving structured or quality information or data from text document. It helps to business for finding valuable knowledge. Sentiment analysis is one of the applications in text mining. In sentiment analysis, determine the emotional tone under the text. It is the major task of natural language processing. The objective of this paper to categorize the document in sentence level and review level, and classification techniques applied on the dataset (electronic product data). There is an ensemble number of classification techniques applied on the dataset. Then compare each techniques, based on various parameters and find out which one is best. According to that give better suggestions to the company for improving the product.

scholarly journals Sentiment Analysis Based Product Selection for Enhancing E-Commerce

2020 ◽  
Vol 9 (3S) ◽  
pp. 389-394
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Basic Problem ◽  
Machine Learning Algorithms ◽  
High Demand ◽  
Product Selection ◽  
Sentence Level ◽  
Selection For

One of the fast growing, developing and highly used technology in various computing industries is data mining. Sentiment or opinion mining is a kind of data mining, where it follows the major processes of natural language processing. Nowadays, sentiment analysis meets a high demand. In this paper, it is aimed to consider the problems of sentiment analysis such as classification on opinion and attribute words, because it is the basic problem of sentiment analysis. This paper aimed to use one of the popular machine learning algorithms as MultiClass Support Machine algorithm for classifying sentiment polarity with detailed description. The proposed method is implemented in Python software and experimented on onlineproduct-reviews data taken from Amazon.com. Sentence level and opinion level classification is obtained with promised outcomes. From the results it is noted that the proposed method outperforms than the existing method such as Naïve Bayes and Random Forest algorithms

Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

2021 ◽  
Vol 20 (4) ◽  
pp. 1-22
Author(s):  
Dang Van Thin ◽  
Ngan Luu-Thuy Nguyen ◽  
Tri Minh Truong ◽  
Lac Si Le ◽  
Duy Tin Vo
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Network Architectures ◽  
Low Resource ◽  
The Neural Network ◽  
Sentence Level ◽  
Push Forward ◽  
Polarity Classification ◽  
Learning Architectures ◽  
Single Approach

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

scholarly journals Plagiarism Detection Process using Data Mining Techniques

2017 ◽  
Vol 5 (4) ◽  
pp. 68
Author(s):  
Mahwish Abid ◽  
Muhammad Usman ◽  
Muhammad Waleed Ashraf
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Computer Systems ◽  
Plagiarism Detection ◽  
Data Mining Techniques ◽  
Detection Process ◽  
Using Data ◽  
Day By Day

<strong>As the technology is growing very fast and usage of computer systems is increased  as compared to the old times, plagiarism is the phenomenon which is increasing day by day. Wrongful appropriation of someone else’s work is known as plagiarism. Manually detection of plagiarism is difficult so this process should be automated. There are various tools which can be used for plagiarism detection. Some works on intrinsic plagiarism while other work on extrinsic plagiarism. Data mining the field which can help in detecting the plagiarism as well as can help to improve the efficiency of the process. Different data mining techniques can be used to detect plagiarism. Text mining, clustering, bi-gram, tri-grams, n-grams are the techniques which can help in this process</strong>

scholarly journals Sentiment Analysis through Machine Learning

2021 ◽  
Vol 56 (3) ◽  
pp. 384-393
Author(s):  
Md. Abbas Ali Khan ◽  
Ali-Emran ◽  
Md. Alamgir Kabir ◽  
Mohammad Hanif Ali ◽  
A. K. M. Fazlul Haque
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Distance ◽  
The Internet ◽  
Text Document ◽  
Ride Sharing ◽  
Wheeled Vehicle ◽  
The People ◽  
Day By Day ◽  
Rainy Days

In recent years, App-Based Transportation System (ABTS) like Ride Sharing (Uber, Patho) has become popular day by day. For our daily life, a rickshaw (a 3-wheeled vehicle usually for one or two passengers that one man pulls) is most important for a short distance. If we add this vehicle to our ABTS system, it will be very much helpful for us, specifically for the rainy season in Bangladesh. On heavy rainy days, in our city Dhaka, other vehicles like CNG, cars, and bikes become unused because roads go underwater. However, the man who pulled the rickshaw can serve this condition. It is more important than the conventional rickshaw is unable to provide such service properly. In this regard, we are proposing an App-Based Rickshaw (ABR), which is convenient to get over distance through the internet. To do this, we have collected data through close questionnaires’ from several types of people. In contrast, collected data are based on a text document. So our aim is to Sentiment Analysis (SA) of the people through machine learning and checks the feasibility of applicability in the real world.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

scholarly journals Implementation Analysis of Data Classification Approach for Sentiment Classification

2021 ◽  
Vol 9 (VII) ◽  
pp. 1509-1512
Author(s):  
Bhushan R. Chincholkar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Analysis ◽  
Hate Speech ◽  
Modern Technology ◽  
Quality Information ◽  
Classification Approach ◽  
Potential Benefits ◽  
Better Than

Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has bee n a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods also as well as some existing fuzzy approaches. Afterword comparing the performance of proposed approach with commonly used sentiment classifiers which are known to perform well in this task. The experimental results indicate that our modified approach performs marginally better than the other algorithms.

scholarly journals Using Ensemble Models to Classify the Sentiment Expressed in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8931 ◽  
Author(s):  
James A. McCart ◽  
Dezon K. Finch ◽  
Jay Jarman ◽  
Edward Hickling ◽  
Jason D. Lind ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Regular Expression ◽  
Shared Task ◽  
Suicide Notes ◽  
The Mean ◽  
The U.S

In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F1 score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875).

Automatic NLP for Competitive Intelligence

2008 ◽  
pp. 54-76 ◽  
Author(s):  
Christian Aranha ◽  
Emmanuel Passos
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Natural Language Processing ◽  
Text Mining ◽  
Language Processing ◽  
Entity Recognition ◽  
Competitive Intelligence ◽  
Reference Process ◽  
Processing Information ◽  
Mining Algorithms

This chapter integrates elements from Natural Language Processing, Information Retrieval, Data Mining and Text Mining to support competitive intelligence. It shows how text mining algorithms can attend to three important functionalities of CI: Filtering, Event Alerts and Search. Each of them can be mapped as a different pipeline of NLP tasks. The chapter goes in-depth in NLP techniques like spelling correction, stemming, augmenting, normalization, entity recognition, entity classification, acronyms and co-reference process. Each of them must be used in a specific moment to do a specific job. All these jobs will be integrated in a whole system. These will be ‘assembled’ in a manner specific to each application. The reader’s better understanding of the theories of NLP provided herein will result in a better ´assembly´.

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