scholarly journals Analisis Sentimen Online Review Pengguna Bukalapak Menggunakan Metode Algoritma TF-IDF

2021 ◽  
Vol 2 (2) ◽  
pp. 35-39
Author(s):  
Pradita Eko Prasetyo Utomo ◽  
Manaar Manaar ◽  
Ulfa Khaira ◽  
Tri Suratno
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Online Review ◽  
Retail Market ◽  
Online Retail ◽  
User Reviews ◽  
User Comments ◽  
Speed Up ◽  
To Receive ◽  
Good Rating

Bukalapak is one of the Customer-To Customer (C2C) e-commerce models. This model is the most widely applied and found on e-commerce sites in Indonesia. The Customer-To Customer (C2C) market is currently still dominant in Indonesia's online retail market. Data collected from Euromonitor estimates that the C2C market contributed 3% of the retail market in Indonesia in 2017, while the B2C market contributed 1.7%. One text mining analysis is that sentiment analysis can be applied to companies that issue a product or service and provide services to receive opinions (feedback) from consumers for the product. Sentiment analysis is applied to classify positive, negative, and neutral feedback from consumers so as to speed up and simplify the company's task to review their product deficiencies. The researcher conducted further analysis on Bukalapak user reviews to find out how user comments or opinions were on Bukalapak using the TF-IDF Algorithm method. And it can be concluded that based on customer review reviews in Bukalapak have a good rating or perception of this Vans shoe product. Can be seen from the results of Sentiments, Sentiment Visualization and WordCloud Visualization which shows that positive reviews have a higher frequency of 70%.  

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

Sentiment analysis of low-carbon travel APP user comments based on deep learning

2021 ◽  
Vol 44 ◽  
pp. 101014
Author(s):  
Xing-Min Lin ◽  
Chun-Heng Ho ◽  
Lu-Ting Xia ◽  
Ruo-Yi Zhao
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Low Carbon ◽  
User Comments

Engaging Your Feelings: Emotion Contagion and Public Engagement on Nonprofit Organizations’ Facebook Sites

2021 ◽  
pp. 089976402110573
Author(s):  
Zongchao Cathy Li ◽  
Yi Grace Ji ◽  
Weiting Tao ◽  
Zifei Fay Chen
Keyword(s):  
United States ◽  
Nonprofit Organizations ◽  
Sentiment Analysis ◽  
Public Engagement ◽  
Negative Emotions ◽  
The United States ◽  
Computer Assisted ◽  
Diffusion Effect ◽  
User Comments ◽  
Emotion Contagion

This study investigated nonprofit organizations’ (NPOs) emotion-based content strategies on Facebook and publics’ engagement behaviors. More than 52,000 Facebook posts and corresponding comments were collected from the top 100 NPOs in the United States. The emotion-carrying status and valence of the messages were analyzed with computer-assisted sentiment analysis procedures. Results confirmed emotion-carrying posts and posts with negative emotions led to increased public engagement as indexed by the volumes of likes, shares, and comments. The presence of emotions and valence of the NPOs’ posts were also found to have a diffusion effect on user comments.

scholarly journals Can bronchodilators improve exercise tolerance in COPD patients without dynamic hyperinflation?

2014 ◽  
Vol 40 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Maria Enedina Aquino Scuarcialupi ◽  
Danilo Cortozi Berton ◽  
Priscila Kessar Cordoni ◽  
Selma Denis Squassoni ◽  
Elie Fiss ◽  
...  
Keyword(s):  
Exercise Tolerance ◽  
Randomized Study ◽  
Cardiopulmonary Exercise Testing ◽  
Airflow Obstruction ◽  
Peak Exercise ◽  
Dynamic Hyperinflation ◽  
Copd Patients ◽  
Improve Exercise Tolerance ◽  
Speed Up ◽  
To Receive

OBJECTIVE: To investigate the modulatory effects that dynamic hyperinflation (DH), defined as a reduction in inspiratory capacity (IC), has on exercise tolerance after bronchodilator in patients with COPD. METHODS: An experimental, randomized study involving 30 COPD patients without severe hypoxemia. At baseline, the patients underwent clinical assessment, spirometry, and incremental cardiopulmonary exercise testing (CPET). On two subsequent visits, the patients were randomized to receive a combination of inhaled fenoterol/ipratropium or placebo. All patients then underwent spirometry and submaximal CPET at constant speed up to the limit of tolerance (Tlim). The patients who showed ΔIC(peak-rest) < 0 were considered to present with DH (DH+). RESULTS: In this sample, 21 patients (70%) had DH. The DH+ patients had higher airflow obstruction and lower Tlim than did the patients without DH (DH-). Despite equivalent improvement in FEV1 after bronchodilator, the DH- group showed higher ΔIC(bronchodilator-placebo) at rest in relation to the DH+ group (p < 0.05). However, this was not found in relation to ΔIC at peak exercise between DH+ and DH- groups (0.19 ± 0.17 L vs. 0.17 ± 0.15 L, p > 0.05). In addition, both groups showed similar improvements in Tlim after bronchodilator (median [interquartile range]: 22% [3-60%] vs. 10% [3-53%]; p > 0.05). CONCLUSIONS: Improvement in TLim was associated with an increase in IC at rest after bronchodilator in HD- patients with COPD. However, even without that improvement, COPD patients can present with greater exercise tolerance after bronchodilator provided that they develop DH during exercise.

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

scholarly journals Sentiment Analysis using Deep Belief Network for User Rating Classification

2021 ◽  
Vol 10 (8) ◽  
pp. 87-91
Author(s):  
Ravi Chandra ◽  
Basavaraj Vaddatti
Keyword(s):  
Sentiment Analysis ◽  
Feature Vector ◽  
Deep Belief Network ◽  
Deep Belief Networks ◽  
User Reviews ◽  
Belief Network ◽  
Precision And Accuracy ◽  
People’S Attitudes ◽  
User Assessment ◽  
Different Levels

People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.

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