Sentiment Analysis of Twitter Data in Online Social Network

2019 ◽  
Author(s):  
Sanjeev Dhawan ◽  
Kulvinder Singh ◽  
Priyanka Chauhan
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Twitter Data

scholarly journals Study of Automotive Brands Popularity in Indonesia Using Twitter Data

2016 ◽  
Vol 3 (1) ◽  
pp. 23-33
Author(s):  
Stevent Efendi ◽  
Alva Erwin ◽  
Kho I Eng
Keyword(s):  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Automotive Industry ◽  
Real World ◽  
The Internet ◽  
Brand Preference ◽  
Twitter Data ◽  
Wide Range ◽  
Widespread Phenomenon

Social media has been a widespread phenomenon in the recent years. People shared a lot of thought in social media, and these data posted on the internet could be used for study and researches. As one of the fastest growing social network, Twitter is a particularly popular social media to be studied because it allows researchers to access their data. This research will look the correlation between Twitter chatter of a brand and the sales of brands in Indonesia. Factors such as sentiment and tweet rate are expected to be able to predict the popularity of a brand. Being one of the biggest industries in Indonesia, automotive industry is an interesting subject to study. A wide range of people buys vehicles, and even gather as communities based on their car or motorcycle brand preference. The Twitter results of sentiment analysis and tweet rate will be compared with real world sales results published by GAIKINDO and AISI.

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 Severity on Location-Based Social Network (LBSN) Data of Natural disasters

2020 ◽  
Vol 8 (5) ◽  
pp. 4219-4224
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Twitter Data ◽  
Location Based Social Network ◽  
Negative Sentiment ◽  
Disaster Event ◽  
Extract Information

Social media emerged as one of the key components to reach disaster affected people, as they supplement planning and operational coordination. Sentiment analysis was expended to identify, extract or characterize subjective information, such as opinions, expressed in a tweet. The sentiment expressed is analyzed and is classified as positive or negative sentiment, which is not versatile enough to capture the exact sentiment conveyed by the user. Opinion mining is a machine learning process used to extract information conveyed by the user in the form of text. In this paper, the lexical analysis to sentiment analysis of twitter data is employed. Conventionally, the sentiment is conveyed using the polarity of the data but in this paper, sentiment intensity is employed to convey the sentiments. Performing sentiment analysis on tweets gives us the sentiment intensity conveyed by the user, which in turn is used to calculate the severity of the disaster event specified by the user. Further, it is also used to classify the tweets based on their severity. This paper proposes a methodology to extract relevant sentiment information from Location Based Social Network (LBSN) and suggests a unique scale to classify this information to help disaster management authority.

A social network based approach to identify and rank influential nodes for smart city

Kybernetes ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bharat Arun Tidke ◽  
Rupa Mehta ◽  
Dipti Rana ◽  
Divyani Mittal ◽  
Pooja Suthar
Keyword(s):  
Social Network ◽  
Smart City ◽  
Large Scale ◽  
Urban Governance ◽  
Online Social Network ◽  
City Development ◽  
Content Type ◽  
Twitter Data ◽  
Heterogeneous Features ◽  
Influential Nodes

Purpose In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes. Design/methodology/approach The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features. Findings The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines. Originality/value Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

scholarly journals Data mining of social manifestations in Twitter: An ETL approach focused on sentiment analysis

2017 ◽  
Author(s):  
Marcela Yagui ◽  
Luís Maia
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Null Hypothesis ◽  
Learning Algorithm ◽  
Online Social Network ◽  
Probabilistic Learning ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Collection Period

The objective of this study was to analyze sentiments of users of online social network twitter to understand how people manifested toward the article published by the magazine Veja on 04-18-16 entitled "bela, recatada e do lar" (beautiful, demure and from home) in an attempt to understand how this behavior evolved in two weeks and to assess which events had aroused greater reaction from people. To this end, a data mining technique known as sentiment analysis was used with the help of the ETL (Extract, Transform and Load) methodology and the Naive Bayes probabilistic learning algorithm. Moreover, the null hypothesis was formulated and tested to see whether two events that took place during the collection period influenced, in fact, the polarity of analyzed sentiments in the generated database.

Knowledge sharing discourse types used by key actors in online affinity spaces

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priya Sharma ◽  
Qiyuan Li ◽  
Susan M. Land
Keyword(s):  
Social Network ◽  
Knowledge Sharing ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Rule Mining ◽  
Content Type ◽  
Network Analyses ◽  
Affinity Spaces ◽  
Highly Active ◽  
Affinity Space

Purpose The growth of online social network sites and their conceptualization as affinity spaces makes them well suited for exploring how individuals share knowledge and practices around specific interests or affinities. The purpose of this study is to extend what is known about highly active/key actors in online affinity spaces, especially the ways in which they sustain and contribute to knowledge sharing. Design/methodology/approach This study analyzed 514 discussion posts gathered from an online affinity space on disease management. This study used a variety of methods to answer the research questions: the authors used discourse analyses to examine the conversations in the online affinity space, social network analyses to identify the structure of participation in the space and association rule mining and sentiment analysis to identify co-occurrence of discourse codes and sentiment of the discussions. Findings The results indicate that the quality and type of discourse varies considerably between key and other actors. Key actors’ discourse in the network serves to elaborate on and explain ideas and concepts, whereas other actors provide a more supportive role and engage primarily in storytelling. Originality/value This work extends what is known about informal mentoring and the role of key actors within affinity spaces by identifying specific discourse types and types of knowledge sharing that are characteristic of key actors. Also, this study provides an example of the use of a combination of rule mining association and sentiment analysis to characterize the nature of the affinity space.

Sign in / Sign up

Export Citation Format

Share Document