Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5

2016 ◽  
Vol 40 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Yoosin Kim ◽  
Rahul Dwivedi ◽  
Jie Zhang ◽  
Seung Ryul Jeong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Purchase Intention ◽  
Opinion Mining ◽  
Sales Performance ◽  
Competitive Intelligence ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Media Data

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

scholarly journals Bi-Lingual (English, Punjabi) Sarcastic Sentiment Analysis by using Classification Methods

2019 ◽  
Vol 8 (9) ◽  
pp. 1383-1388
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Step ◽  
Online Data ◽  
Social Media Data ◽  
The Social ◽  
Negative Comments ◽  
Regional Languages ◽  
Media Data ◽  
Day By Day

Sentiment analysis is one of the heated topic in the field of text mining. As the social media data is increased day by day the main need of the data scientists is to classify the data so that it can be further used for decision making or knowledge discovery. Now –a-days everything and everyone available online so to check the latest trends in business or in daily life one must consider the online data. The main focus of sentiment analysis is to focus on positive or negative comments so that a well define picture is created that what is trending or not but the sarcasm manipulates the data as in sarcastic comment negative comment consider as positive because of the presence of positive words in the comment or data so it is necessary to detect the sarcasm in online data . The data on social media is available in various languages so sentiment analysis in regional languages is also a main step . In the proposed work we focus on two languages i.e Punjabi and English. Here we use deep learning based neural networks for the sarcasm detection in English as well as Punjabi language. In the proposed work we consider three datasets i.e. balanced English dataset, Balanced Punjabi Dataset and unbalanced Punjabi dataset. We used six different models to check the accuracy of the classified data the models we used are LSTM with word embedding layer, BiLSTM with , LSTM+LSTM, BiLSTM+BiLSTM, LSTM+BiLSTM, CNN respectively. LSTM provide better accuracy for balanced Punjabi and English dataset i.e. 95.63% and 94.17% respectively. The accuracy for unbalanced Punjabi dataset is provided by BiLSTM i.e.96.31%.

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals SCRAPING OF SOCIAL MEDIA DATA USING PYTHON-3 AND PERFORMING DATA ANALYTICS USING MICROSOFT POWER BI.

2020 ◽  
Vol 9 (7) ◽  
pp. 66-79
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Marketing Strategies ◽  
Social Media Data ◽  
The Social ◽  
The Right ◽  
Media Data ◽  
Course Of Study

The manifestation of humanity is driven by fulfillment of desires. These desires are satiated by the society and its resources. But after the advent of social media the societal boundaries have shrunken but desires haven’t, hence the desires are now fulfilled through social media. The aforementioned phenomenon was recognized by the business plutocrats very early and have started to satisfy human desires using social media as a tool. But before satisfying the desires, the businesses needs to identify the specific desires of an individual. The identification of specific desires/needs will help the marketing agencies to develop user specific marketing strategies. These desires are explicitly available through the expressions of sentiments in the social media. The sentiment analysis can provide an insight to the desires of an individual. These patterns and insights helps the businesses to market their product to the right person. The sentiments and expressions can be captured using the scraping technique. The aforesaid points highlight’s the course of study followed by this paper and it is to perform data analytics of the social media data scraped using python.

Exploring the influence of multimodal social media data on stock performance: an empirical perspective and analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hui Yuan ◽  
Yuanyuan Tang ◽  
Wei Xu ◽  
Raymond Yiu Keung Lau
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Textual Information ◽  
Stock Performance ◽  
Vector Autoregressive Models ◽  
Social Media Data ◽  
Content Type ◽  
Vector Autoregressive ◽  
Media Sentiment ◽  
Media Data

PurposeDespite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.Design/methodology/approachThis research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.FindingsThe empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.Originality/valueTo the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Feng-Kwei Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
Response Strategies ◽  
Related Information ◽  
Valence And Arousal ◽  
Public Reaction ◽  
Media Data

PurposeIn recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.Design/methodology/approachThis study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.FindingsThis research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.Originality/valueThis study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

To sell, or not to sell: social media data-breach in second-hand Android devices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oussama BenRhouma ◽  
Ali AlZahrani ◽  
Ahmad AlKhodre ◽  
Abdallah Namoun ◽  
Wasim Ahmad Bhat
Keyword(s):  
Social Media ◽  
Social Network ◽  
Use Cases ◽  
Road Accidents ◽  
Data Breach ◽  
Social Media Data ◽  
Content Type ◽  
Private Data ◽  
The Social ◽  
Media Data

Purpose The purpose of this paper is to investigate the private-data pertaining to the interaction of users with social media applications that can be recovered from second-hand Android devices. Design/methodology/approach This study uses a black-box testing-principles based methodology to develop use-cases that simulate real-world case-scenarios of the activities performed by the users on the social media application. The authors executed these use-cases in a controlled experiment and examined the Android smartphone to recover the private-data pertaining to these use-cases. Findings The results suggest that the social media data recovered from Android devices can reveal a complete timeline of activities performed by the user, identify all the videos watched, uploaded, shared and deleted by the user, disclose the username and user-id of the user, unveil the email addresses used by the user to download the application and share the videos with other users and expose the social network of the user on the platform. Forensic investigators may find this data helpful in investigating crimes such as cyber bullying, racism, blasphemy, vehicle thefts, road accidents and so on. However, this data-breach in Android devices is a threat to user's privacy, identity and profiling in second-hand market. Practical implications Perceived notion of data sanitisation as a result of application removal and factory-reset can have serious implications. Though being helpful to forensic investigators, it leaves the user vulnerable to privacy breach, identity theft, profiling and social network revealing in second-hand market. At the same time, users' sensitivity towards data-breach might compel users to refrain from selling their Android devices in second-hand market and hamper device recycling. Originality/value This study attempts to bridge the literature gap in social media data-breach in second-hand Android devices by experimentally determining the extent of the breach. The findings of this study can help digital forensic investigators in solving crimes such as vehicle theft, road accidents, cybercrimes and so on. It can assist smartphone users to decide whether to sell their smartphones in a second-hand market, and at the same time encourage developers and researchers to design methods of social media data sanitisation.

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emilio Pindado ◽  
Ramo Barrena
Keyword(s):  
Social Media ◽  
Clustering Algorithm ◽  
Social Representations ◽  
Local Level ◽  
Geographical Information ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
Food Trends ◽  
Media Data

PurposeThis paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.Design/methodology/approachA density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.FindingsTwitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.Research limitations/implicationsThe social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.Practical implicationsThe approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.Originality/valueThis study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

Determining social media impact on the politics of developing countries using social network analytics

2016 ◽  
Vol 50 (4) ◽  
pp. 481-507 ◽  
Author(s):  
Collins Udanor ◽  
Stephen Aneke ◽  
Blessing Ogechi Ogbuokiri
Keyword(s):  
Social Media ◽  
Developing Countries ◽  
Developing Economies ◽  
General Election ◽  
Typical Application ◽  
Social Media Data ◽  
Content Type ◽  
The Social ◽  
The Impact ◽  
Media Data

Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

Identifying customer knowledge on social media through data analytics

2019 ◽  
Vol 32 (1) ◽  
pp. 152-169 ◽  
Author(s):  
Wu He ◽  
Weidong Zhang ◽  
Xin Tian ◽  
Ran Tao ◽  
Vasudeva Akula
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Social Media Analytics ◽  
Data Set ◽  
Social Media Data ◽  
Customer Knowledge ◽  
Content Type ◽  
Customer Knowledge Management ◽  
The Social ◽  
Media Data

Purpose Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management. Design/methodology/approach The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge. Findings The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers. Originality/value This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

scholarly journals Network based model of social media big data predicts contagious disease diffusion

2017 ◽  
Vol 45 (3) ◽  
pp. 110-120 ◽  
Author(s):  
Lauren S. Elkin ◽  
Kamil Topal ◽  
Gurkan Bebek
Keyword(s):  
Social Media ◽  
Big Data ◽  
Disease Outbreaks ◽  
Social Media Data ◽  
Content Type ◽  
Contagious Diseases ◽  
The Social ◽  
The Usa ◽  
Disease Diffusion ◽  
Media Data

Purpose Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks. Design/methodology/approach In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data. Findings The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets. Originality/value First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

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