scholarly journals A Study on Opinion Mining of Newspaper Texts based on Topic Modeling

2013 ◽  
Vol 47 (4) ◽  
pp. 315-334 ◽  
Author(s):  
Beomil Kang ◽  
Min Song ◽  
Whasun Jho
Keyword(s):  
Topic Modeling ◽  
Opinion Mining

scholarly journals Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 535 ◽  
Author(s):  
Alejandro Ramón-Hernández ◽  
Alfredo Simón-Cuevas ◽  
María Matilde García Lorenzo ◽  
Leticia Arco ◽  
Jesús Serrano-Guerrero
Keyword(s):  
Topic Modeling ◽  
Social Impact ◽  
Opinion Mining ◽  
Relevant Information ◽  
User Generated Content ◽  
Context Aware ◽  
Government Programs ◽  
Digital Platforms ◽  
Opinion Summarization ◽  
Digital Governance

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

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.

scholarly journals Study of the Yahoo-yahoo Hash-tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms

2021 ◽  
Author(s):  
Adebayo Abayomi-Alli ◽  
Olusola Abayomi-Alli ◽  
Sanjay Misra ◽  
Luis Fernandez-Sanz
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Latent Semantic Indexing ◽  
Workflow Engine ◽  
Semantic Indexing ◽  
Short Period ◽  
Mining Algorithms ◽  
Insight Into

Abstract BackgroundSocial media opinion has become a medium to quickly access large, valuable, and rich details of information on any subject matter within a short period. Twitter being a social microblog site, generate over 330 million tweets monthly across different countries. Analyzing trending topics on Twitter presents opportunities to extract meaningful insight into different opinions on various issues.AimThis study aims to gain insights into the trending yahoo-yahoo topic on Twitter using content analysis of selected historical tweets.MethodologyThe widgets and workflow engine in the Orange Data mining toolbox were employed for all the text mining tasks. 5500 tweets were collected from Twitter using the 'yahoo yahoo' hashtag. The corpus was pre-processed using a pre-trained tweet tokenizer, Valence Aware Dictionary for Sentiment Reasoning (VADER) was used for the sentiment and opinion mining, Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) was used for topic modeling. In contrast, Multidimensional scaling (MDS) was used to visualize the modeled topics. ResultsResults showed that "yahoo" appeared in the corpus 9555 times, 175 unique tweets were returned after duplicate removal. Contrary to expectation, Spain had the highest number of participants tweeting on the 'yahoo yahoo' topic within the period. The result of Vader sentiment analysis returned 35.85%, 24.53%, 15.09%, and 24.53%, negative, neutral, no-zone, and positive sentiment tweets, respectively. The word yahoo was highly representative of the LDA topics 1, 3, 4, 6, and LSI topic 1.ConclusionIt can be concluded that emojis are even more representative of the sentiments in tweets faster than the textual contents. Also, despite popular belief, a significant number of youths regard cybercrime as a detriment to society.

How does Social Media Analytics Create Value?

2016 ◽  
Vol 28 (3) ◽  
pp. 1-9 ◽  
Author(s):  
Shahriar Akter ◽  
Mithu Bhattacharyya ◽  
Samuel Fosso Wamba ◽  
Sutapa Aditya
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Opinion Mining ◽  
Predictive Analysis ◽  
Social Media Analytics ◽  
Competitive Advantages ◽  
Social Data ◽  
Know How ◽  
Different Types ◽  
Challenges And Opportunities

The surge of interest in big social data has led to growing demand for social media analytics (SMA). Having robust SMA can help firms create value and achieve competitive advantages. However, most firms do not always know how to embrace big social data to establish a path to value. This study addresses this key question to deepen our understanding of how different types of SMA can be applied to create value. Specifically, the findings show the significant uses of opinion mining or sentiment analysis, topic modeling, engagement analysis, predictive analysis, social network analysis, and trend analysis. Finally, the study provides directions for the challenges and opportunities of SMA to maximize value.

scholarly journals Opinion mining framework applied to a social networks data for small and medium enterprises

2020 ◽  
Vol 20 (3) ◽  
pp. 167-192
Author(s):  
Huoston Rodrigues Batista ◽  
Marcos Antonio Gaspar ◽  
Renato José Sassi
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
English Language ◽  
Opinion Mining ◽  
Small And Medium Enterprises ◽  
The Social ◽  
Social Contributions ◽  
Medium Enterprises ◽  
Almost All

2) Objective: To present a framework for the mining of opinions that can be applied in the discovery of knowledge of the customers about to their experiences, based on unstructured data extracted from social networks, and that is applicable to the reality of small and medium enterprises.3) Methodology: This experimental research accessed data from the opinions of customers of four restaurants published in the social network TripAdvisor Brazil. The framework was based on the proposals formulated by Aranha (2007) and Feldman and Sanger (2007), techniques for Sentiment Analysis by Liu (2012) and Pang and Lee (2008) and Topic Modeling by Blei et al. (2012).4) Originality: The relevance consists in proposing a solution that is both accessible to SMEs and capable of processing opinions in Portuguese, something not very common in literature. Almost all similar applications in literature are dedicated to the English language.5) Main results: We highlight the generation of summaries and graphic visualizations that contribute to evidence knowledge about the relations between several expressions and terms that were not obvious. These allowed finding latent relationships between terms cited by different customers.6) Theoretical contributions: The methodological solution uses efficient and state-of-the-art techniques and methods to extract, process, and analyze customer opinions on the Internet quickly, efficiently, and economically.7) Social contributions: the framework developed presents an efficient, fast and economical way to mine data, presenting the results of the discovery of customer knowledge through the use of Sentiment Analysis and Topic Modeling techniques.

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

2020 ◽  
Vol 5 (1) ◽  
pp. 86-99
Author(s):  
Runbin Xie ◽  
Samuel Kai Wah Chu ◽  
Dickson Kak Wah Chiu ◽  
Yangshu Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Disease Outbreaks ◽  
Semantic Networks ◽  
Web Crawler ◽  
Public Response ◽  
The Public

AbstractIt is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.

Sign in / Sign up

Export Citation Format

Share Document