Sentiment Analysis of English Tweets Using Data Mining

2018 ◽  
Vol 6 (10) ◽  
pp. 276-284
Author(s):  
Amritpal Kaur ◽  
Seema Baghla
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Using Data

Sentiment analysis of foreign tourists to Bangkok using data mining through online social network

2017 ◽  
Author(s):  
Taweesak Kuhamanee ◽  
Nattaphon Talmongkol ◽  
Krit Chaisuriyakul ◽  
Wimol San-Um ◽  
Noppadon Pongpisuttinun ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Using Data

scholarly journals Sentiment Analysis and Summarization of Social Media Content using Topic Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 209-212
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Opinion Mining ◽  
Text Documents ◽  
Topic Identification ◽  
Key Factor ◽  
Polarity Classification ◽  
Using Data

Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative & Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents & Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.

Who creates value in a user innovation community? A case study of MyStarbucksIdea.com

2016 ◽  
Vol 40 (2) ◽  
pp. 170-186 ◽  
Author(s):  
Hanjun Lee ◽  
Yongmoo Suh
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Open Innovation ◽  
Information Overload ◽  
Classification Models ◽  
Content Type ◽  
Data Mining Techniques ◽  
Using Data ◽  
Basic Features ◽  
Innovation Community

Purpose – Successful open innovation requires that many ideas be posted by a number of users and that the posted ideas be evaluated to find ideas of high quality. As such, successful open innovation community would have inherently information overload problem. The purpose of this paper is to mitigate the information problem by identifying potential idea launchers, so that they can pay attention to their ideas. Design/methodology/approach – This research chose MyStarbucksIdea.com as a target innovation community where users freely share their ideas and comments. We extracted basic features from idea, comment and user information and added further features obtained from sentiment analysis on ideas and comments. Those features are used to develop classification models to identify potential idea launchers, using data mining techniques such as artificial neural network, decision tree and Bayesian network. Findings – The results show that the number of ideas posted and the number of comments posted are the most significant among the features. And most of comment-related sentiment features found to be meaningful, while most of idea-related sentiment features are not in the prediction of idea launchers. In addition, this study show classification rules for the identification of potential idea launchers. Originality/value – This study dealt with information overload problem in an open innovation context. A large volume of textual customer contents from an innovation community were examined and classification models to mitigate the problem were proposed using sentiment analysis and data mining techniques. Experimental results show that the proposed classification models can help the firm identify potential idea launchers for its efficient business innovation.

scholarly journals SENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING

2020 ◽  
Vol 17 (2) ◽  
pp. 143-150
Author(s):  
Irwansyah Saputra ◽  
Jose Andrean Halomoan ◽  
Adam Bagusmugi Raharjo ◽  
Cyra Rezky Ananda Syavira
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Bayes Classifier ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Processing Data ◽  
Twitter Users ◽  
Using Data

A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%.

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