Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Public Opinion Analysis Using Hadoop

Recent technological advances in devices, computing, and social networking have revolutionized the world but have also increased the amount of data produced by humans on a large scale. If you collect this data in the form of disks, it may fill an entire football field. According to studies, 2.5 billion gigabytes of new data is generated every day and 2.5 petabytes of data is collected every hour. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it gets neglected. Social media has gained massive popularity nowadays. Twitter makes it easy to engage users in expressing, sharing and discussing hot latest topics but these public expressions and views are hard to analyze due to the bigger size of the data created by Twitter. In order to perform analysis and predictions over the hot topics in society, latest technologies are needed. The most popular solution for this is Hadoop. Hadoop acts as an open-source framework for developing and executing distributed applications that process very large amounts of data. It stores and process big data in a distributed fashion on large clusters of commodity hardware. The risk, of course, in running on commodity machines is how to handle failure. Hadoop is built with the assumption that hardware will fail and as such, it can easily handle most failures. Hadoop can be used for developing and executing distributed applications that process very large amounts of data. It provides a suitable environment needed for treating or processing huge data. Our job is to extract and store data into its file system and query the data according to the desired output. We propose to perform analysis on Public opinion expressed over Twitter regarding the trending topics of the society by using Apache Hadoop framework along with its services Apache Flume and Apache Hive

Tapping Twitter Data for Analyzing and Visualizing Public Sentiments on Censorship

The main objective of this research study is to analyse and visualize Twitter data with tags “#Censorship”. A connection was established with twitter using Twitter API, and receiving the tweets on Google Spreadsheets. Data visualization was performed using various tools such as Voyant Tools, Tableau, Google Spreadsheet and Orange in order to generate different visualizations based upon, language, geographical areas, retweets etc. The sentiment analysis was performed for the sentiments that were attached to the given set of data by the public in their respective tweets. The 23680 tweets were retrieved during the data collection time and there were 13,771 retweets out of these tweets. The most popular application for using twitter by the users was Twitter Web Client which constituted of the 33.67% (7972 tweets); the second most popular app was Android with 23.61% (5592 Tweets) and Twitter for iPhone stayed at third place with a share of 20.07% (4753 Tweets). The most frequent used Hashtags (#) in the tweets were #Twitter, #Facebook, #Google, #YouTube etc. Results show that negative tweets are enormously higher than the neutral sentiments

Sentiment Analysis of Spanish Words of Arabic Origin Related to Islam: A Social Network Analysis

With the arrival of Muslims in 711 till their expulsion in the 1600s, Arabic language was present in Spain for more than eight centuries. Although social networks have become a valuable resource for mining sentiments, there is no previous research investigating the layman’s sentiment towards Spanish words of Arabic etymology related to Islamic terminology. This study aim at analyzing Spanish words of Arabic origin related to Islam. A random sample of 4586 out of 45860 tweets was used to evaluate general sentiment towards some Spanish words of Arabic origin related to Islam. An expert-predefined Spanish lexicon of around 6800 seed adjectives was used to conduct the analysis. Results indicate a generally positive sentiment towards several Spanish words of Arabic etymology related to Islam. By implementing both a qualitative and quantitative methodology to analyze tweets’ sentiments towards Spanish words of Arabic etymology, this research adds breadth and depth to the debate over Arabic linguistic influence on Spanish vocabulary

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

Opinion Mining, Sentiment Analysis and Emotion Understanding in Advertising: A Bibliometric Analysis

In the last decade, the advertising industry has experienced a quantum leap, powered by recent advances in neuroscience, a large investment in artificial intelligence, and a high degree of consumer expertise. Within this context, opinion mining, sentiment analysis, and emotion understanding bring us closer to one of the most sought-after objectives of advertising: to offer relevant ads at scale. The importance of studies about opinion mining, sentiment analysis, and emotion understanding in advertising has been rising exponentially over the last years. The peak of this new situation has been the interest of the research community in studying the relationship between such innovations and the spread of smart and contextual advertising. This article analyzes those works that address the relationship between sentiment analysis, opinion mining, and emotion understanding in advertising. The main objective is to clarify the current state of these studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within the current convergence advertising research scenario. To reach such objectives, a bibliometric analysis was conducted, retrieving and analyzing 919 research works published between 2010 and 2019 based on results from Web of Science (WoS)

Topic modeling in marketing: recent advances and research opportunities

Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models

Analysis sentiment about islamophobia when Christchurch attack on social media

Islamophobia is formed by "Islam" with "-phobia" which means "fear of Islam". This shows the view of Islam as "other" and can threaten Western culture. The recent horrific terror attack that took place at the Christchurch mosque in New Zealand, is the result of allowing an attitude of hatred towards Islam in the West. Twitter is social media that allows users send real-time messages and can be used for sentiment analysis because it has a large amount of data. The lexical based method using VADER is used for automatic labeling of crawling data from Twitter. And then compare Supervised Machine Learning Naïve Bayes and SVM algorithm. Addition of SMOTE for Imbalanced Data. As result, SVM with SMOTE is proven the highest performance value and short processing time

Analysing and Visualizing Tweets for U.S. President Popularity

In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets