scholarly journals Sentiment Analysis for Product Recommendation System Using Hybrid Algorithm

2019 ◽  
pp. 278-287
Author(s):  
R. Umamaheswari ◽  
G. Kanimozhi
Keyword(s):  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Detection Methods ◽  
Research Subjects ◽  
Content Mining ◽  
Commercial Enterprise ◽  
On Line ◽  
Textual Content

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this paper, finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best and use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

scholarly journals Sentiment Analysis for Product Recommendation System Using Enhanced Stochastic Learning Algorithm

2019 ◽  
pp. 228-234
Author(s):  
S. Gayathri ◽  
K. Thyagarajan
Keyword(s):  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Detection Methods ◽  
Research Subjects ◽  
Content Mining ◽  
Commercial Enterprise ◽  
On Line ◽  
Textual Content

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this project we are finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best. For this we use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

scholarly journals Sentiment Analysis of Events on Social Web

2020 ◽  
Vol 9 (4) ◽  
pp. 1232-1238
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Extraction Process ◽  
Online Data ◽  
Promising Area ◽  
Data Volume ◽  
Application Data

These days, Data volume has experienced enormous increase in volume, giving new challenges in technology and application. Data production has been expected at the rate of 2.5 Exabyte (1Ex-abyte=1.000.000Terabytes) of data per day. The main sources of data are: sensors collect climate information, traffic and flight information, social media sites (Twitter and Facebook are popular examples), digital pictures and videos (YouTube users upload 72 hours of new video content per minute), etc. Out of them social media becomes the prominent representative for the data source of big data. Social big data comes from the combination of social media and big data. Here, the data is mostly unstructured or semi-structured. The classical approaches, techniques, tools and frameworks for management of data have become insufficient for processing this huge volume of data and not capable for providing efficient solution to handle the increased production of data. The major challenge in data mining of big data is, its inadequate approaches to analyze massive amount of online data (or data streams). Specially, the field of sentiment analysis and predictive analysis has become so much promising area to place an organization at doom or at boom by provide accurate decision at accurate time. The current paper provides an insight of machine learning algorithm both supervised and unsupervised method; and the traditional knowledge extraction process. The application field of sentiment analysis, the issues those are faced during data collection and cleaning. This study flourishes a complete picture of recommendation system based on the sentiment analysis of events. The key motivation of the paper is to incorporate the event sentiment analysis and give the feedback and recommendation and illustrate the ongoing researches in the field of sentiment analysis and its application.

scholarly journals The Computational Approach for Recommendation System Based on Tagging Data

2019 ◽  
Vol 16 ◽  
pp. 8359-8367
Author(s):  
Kateryna Nesvit
Keyword(s):  
Numerical Methods ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Computational Approach ◽  
High Quality ◽  
Data Pipeline ◽  
Huge Impact ◽  
Predicted Values ◽  
Comparison Table

Recommendation approaches like a platform for learning algorithm. We can use some predicted values to put them in the data pipeline forlearning. There is a hard nuance of how to calculate the similarity measurewhen we have a small number of actions at all, its not a new user or item to use cold start methods, we just have not enough quantity to say it may be interpreted like regularity. The frequency of tags what we would have fromusers will have a huge impact to predict his future taste. The article describes created a computational approach using as explicit and as implicit feedbacks from users and evaluates tags by Jaccard distance to resolve this issue. To compare results with existed numerical methods there is a comparison table that shows the high quality of the proposed approach.

ACADEMY Journal 7(9), 2018

2018 ◽  
Author(s):  
Liudmila Vyacheslavovna Fomina ◽  
Саидова Феруза Бахтияровна
Keyword(s):  
Review Process ◽  
Final Decision ◽  
Research Subjects ◽  
Open File ◽  
Specific Research ◽  
Blind Review

"Journal of the Academy" isan international,peerreviewedmonthly journal. It is dedicated tothe publication of original scientific articles invarious academic disciplines.Articles that may be of interest to a wide rangeof researchers, welcome, and are not limited tothose who work on specific research subjects."Journal of the Academy" has an open file,according to which the published articles areavailable immediately after its publication, withthe exception of the embargo.ExpertiseThe magazine has a blind review process. Allarticles will initially be evaluated by the editor tomatch the magazine. The manuscripts that areconsidered suitable, are usually sent at leasttwo independent experts to evaluate thescientific quality of the article. The editor isresponsible for the final decision on whether toaccept or reject the article. Editor's decision isfinal.

Increasing Competency Of SD Teachers In Thematic Learning Planning Through Group Supervision Using Collaborative Approaches In SDN Tlambah 2 Karangpenang Sampang

2019 ◽  
Vol 4 (2) ◽  
pp. 44-54
Author(s):  
Namin Namin
Keyword(s):  
Action Research ◽  
Action Observation ◽  
Group Supervision ◽  
Low Grade ◽  
Classroom Teachers ◽  
Collaborative Approach ◽  
Research Subjects ◽  
Thematic Learning ◽  
Planning Action

Abstrak: Penelitian ini dilakukan dengan tujuan meningkatkan kompetensi guru di SDN Tlambah 2, Kabupaten Karangpenang Sampang. Perencanaan pembelajaran tematik dalam meningkatkan kualitas pembelajaran tematik. Subjek penelitian dalam penelitian tindakan kelas ini adalah guru kelas di Sekolah Dasar Negeri Tlambah 2, Kabupaten Karangpenang Sampang. Penelitian tindakan kelas ini dilaksanakan dalam dua siklus, menggunakan tahapan perencanaan, tindakan, observasi dan refleksi dalam setiap siklus. Data yang terkumpul dianalisis secara kuantitatif dan kualitatif. Dari penelitian tindakan kelas ini disimpulkan bahwa supervisi kelompok dengan pendekatan kolaboratif dapat meningkatkan kompetensi guru kelas di SDN Tlambah 2 Kabupaten Karangpenang Sampang.   Kata kunci: kolaboratif, kompetensi, tematik     Abstract: This research was conducted with the aim of increasing the competency of teachers in SDN Tlambah 2, Karangpenang Sampang District, Spreading thematic learning planning in improving the quality of thematic learning. Research subjects in this action research are low grade teachers in Tlambah 2 Public Elementary School, Karangpenang Sampang District. School action research means it is carried out in two cycles, using the stages of planning, action, observation and reflection in each cycle. The collected data is analyzed quantitatively and qualitatively. From this school action research concludes that group supervision with a collaborative approach can improve the competence of classroom teachers in SDN Tlambah 2, Karangpenang Sampang District.   Keywords: collaborative, competence, thematic

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
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.

UPAYA MENINGKATKAN KOMPETENSI GURU DALAM MENYUSUN RENCANA PELAKSANAAN PEMBELAJARAN MELALUI BIMBINGAN BERKELANJUTAN DI SD NEGERI 017 KUNTO DARUSSALAM TAHUN 2017

2020 ◽  
Author(s):  
Sri Winarsih
Keyword(s):  
Learning Strategies ◽  
Learning Process ◽  
School Principal ◽  
Initial Condition ◽  
Research Subjects ◽  
Analysis Techniques ◽  
Pedagogical Competence ◽  
Treatment Data ◽  
The Subject

This study aims to determine the appropriate steps in carrying out academic supervision so as to be able to improve the pedagogical competence of teachers, especially in the learning process which in turn will affect the improvement of the quality of education.The study was conducted in two cycles. Each cycle has different planning, implementation, observation and reflection. Research subjects of the principal and teacher. The school principal with his academic supervision measures, while the Kunto Darussalam Elementary School 017 teacher as an object as well as the subject in providing academic supervision treatment. Data collection techniques through class supervision with stages of supervising teachers in the learning process and observation of classroom learning, to record important events related to research, especially at the time of the processlearning takes place.Data analysis techniques that guide data processing using a percentage (%) of achievement with 100 constants. And to see the interpertation using score interpertation criteria to strengthen the interpretation in conclusions as follows: 80% - 100% (Very Good), 66% - 79 % (Good), 56% - 65% (Enough), and 40% - 55% (Less).The results showed that the ability of teachers in the implementation of the learning process experienced an increase in the percentage at each stage, from the first cycle reached an average of 63% (sufficient) and in the second cycle reached an average of 68% (good). There is an increase in teacher's ability by 5% from cycle I. In detail there is a significant increase in the initial condition of the school when compared to the final condition in the second cycle. The accuracy of teachers entering the class increased by 48%, the use of learning media increased by 32%, varied methods increased by 31%, and learning strategies increased by 36%.

Automatic buffer capacity based sensor for effluent quality monitoring

1996 ◽  
Vol 33 (1) ◽  
pp. 81-87
Author(s):  
L. Van Vooren ◽  
P. Willems ◽  
J. P. Ottoy ◽  
G. C. Vansteenkiste ◽  
W. Verstraete
Keyword(s):  
Wastewater Treatment ◽  
Buffer Capacity ◽  
Wastewater Treatment Plants ◽  
Data Interpretation ◽  
Full Scale ◽  
Quality Monitoring ◽  
Effluent Quality ◽  
Capacity Curves ◽  
On Line

The use of an automatic on-line titration unit for monitoring the effluent quality of wastewater plants is presented. Buffer capacity curves of different effluent types were studied and validation results are presented for both domestic and industrial full-scale wastewater treatment plants. Ammonium and ortho-phosphate monitoring of the effluent were established by using a simple titration device, connected to a data-interpretation unit. The use of this sensor as the activator of an effluent quality proportional sampler is discussed.

Sign in / Sign up

Export Citation Format

Share Document