Social Media and Sentiment Analysis

In this conference paper, we assess the progress in post-disaster recovery by analysing 4349 tweets posted between 4th and 10th April 2019 that we collected around the 10th anniversary of the earthquake in L'Aquila. Text data collected from social media is unstructured; therefore, we need to use natural language processing techniques such as topic and sentiment analysis to extract meaningful information to assess recovery. Sentiment Analysis (SA), or opinion mining, classifies people's opinions, expressed in written text, into a specific polarity, i.e. positive, negative or neutral. Topic Analysis (TA) classifies data into recurrent themes or topics users address in their posts. These analyses can be done at the tweet or sentence level, and the classification into polarities and topics can be done manually or automated. We analysed a sample of 10% of tweets covering 24 hours daily. The SA and TA were performed at both levels, and the classification was done manually. The SA at the tweet level indicates that most of the posts were classified into neutral polarity, followed closely by positive and negative. Similar results were obtained from the SA analysis at the sentence level, only with variation in percentages of the sentence classified into each polarity. The TA at the tweet and sentence levels indicate that the most frequently addressed topics by users at both levels were commemoration actions, restoration, reconstruction, governance, and distress. The SA per topics at the tweet level indicates that the topics with neutral polarity are critical infrastructure, commemoration actions, and seismic information. Topics with positive polarity are cultural heritage, early recovery, emergency response, lifelines, preparedness, restoration, solidarity messages/actions and urban facilities, which are considered successful aspects of the recovery process in this methodology. Topics with negative polarity are building damages, construction practices, depopulation and displacement, distress, governance, injuries and casualties, intensity, prevention, and reconstruction, considered the failures of the process. We also conducted a two-tailed Pearson correlation analysis between polarity and topics of tweets for each day, which confirmed, in most cases, the results of the SA for each topic at the tweet level. According to the methodology applied, we can conclude that the perception of the recovery of L'Aquila by the 10th anniversary is mainly neutral.

We investigate the mediating role of moral emotions and their contingency on individual characteristics in consumer responses to corporate green and non-green actions. Two between subjects experiments were conducted to test our hypotheses on samples of adult consumers. The results show that, for corporate non-green actions, various individual difference characteristics (social justice values, empathy, moral identity, self-concept) moderate the elicitation of negative moral emotions (contempt, anger, disgust), which, in turn, lead to consumer negative responses (negative word of mouth, complaint behaviors, boycotting). Moreover, for corporate green actions, empathy moderates elicitation of positive emotions on gratitude, which, in turn, influences consumer positive responses (positive word of mouth, resistance to negative information, identification with the company, investment). This study adds to extant research by examining understudied "hot" moral emotional processes underlying consumer reactions toward corporate environmental responsibility and irresponsibility. Implications for marketing communication and segmentation decisions are considered.

Sentiment analysis has become one of the most common method to classify stock market behaviour. Moreover, sentiment analysis has gained a lot of importance in the last decade especially due to the availability of data from social media such as Twitter. However, the accuracy of stock market classification models is still low, and this has negatively affected the stock market indicators. In this research, a model for GCC stock market classification based on sentiment analysis is constructed. It is designed to enhance the classification accuracy by the incorporation of tweet timestamp and location features, stock market domain expert labelling technique and the construction of a hybrid Naïve Bayes classifiers to classify the stock market sentiments. The methodology for this research consists of six phases. Data collection, labelling technique, data pre-processing, classification, performance and evaluation, and the final phase is recognition for the stock market behaviour. The model produced a significant result in classifying stock market behaviour with accuracy more than 89%. The model is beneficial for investors and researchers. For investors, it enables them to formulate their plans based on accurate indicators whereby it reduces the risk in decision making. For researchers, it draws their attention to the importance of feature engineering, labelling technique, and the classifiers hybridization in enhancing the classification accuracy.

Recovery is a complex multidimensional long-term process of restoration of living conditions after a disaster. Memorial days of disasters represent an opportunity to evaluate the progress of these recovery processes. We evaluated Nepal's recovery progress based on text data posted on Twitter and Instagram from March 5th to May 2nd, 2020. This period includes dates before and after the 5th anniversary of the Gorkha earthquake (2015, 7.8Mw): April 25th, 2020. Hashtags in tweets related to the anniversary were: #eartHquake, #Nepal, #earthquake, #Nepalearthquake, #NepalQuake, #5yearson, and #Nepali. Text data collected from social media is unstructured data. In this paper, we used sentiment and topic analysis to extract meaningful information from the data. Through sentiment analysis (SA), we classified tweets according to their polarity: positive, negative or neutral, while with topic analyses, we classified tweets into topics. Both actions were performed to structure the data and extract meaningful information. We focused on analysing tweets three hours before and after the earthquake's exact anniversary, considering two hours: 11:56:25 Nepal standard time and 11:56:25 UTC resulting in an analysing period of twelve hours. Eventually, 2005 tweets were manually classified according to their polarity at the tweet level as 1011 (50%) tweets unrelated, 525 (26%) positive, 366 (18%) negative and 103 (5%) neutral The most frequent topics identified in tweets, without counting unrelated: 329 (16%) COVID-19, 286 (14%) recovery, 91 (5%) commemoration actions, 85 (4%) emergency response, 51 (3%) governance, 51 (3%) reported reconstruction, 22 solidarity (1%) and 18 (1%) distress. According to these sentiment and topic analyses at the tweet and sentence level and those SA analyses of tweets addressing recovery exclusively, Nepal's assessment of the recovery and process is positively, going beyond reconstruction actions, despite the inconveniences caused by COVID-19 in the fifth year.

In this research, we develop a new emotion scale that applies to both media titles and advertiser brands. The dimensions include a positive affective dimension, plenitude; a negative dimension fear; and a mixed dimension, possession, which includes a rather negative facet, envy, and a rather positive one, love. This research also studies the effects of emotions on affective relational variables, and the potential mediating role of attachment on emotional commitment. [url]: http://www.acrwebsite.org/volumes/15942/volumes/v38/NA-38 [copyright notice]: This work is copyrighted by The Association for Consumer Research. For permission to copy or use this work in whole or in part, please contact the Copyright Clearance Center at http://www.copyright.com/. 322 Advances in Consumer Research Volume 38, © 2012 ABSTRACT In this research, we develop a new emotion scale that applies to both media titles and advertiser brands. The dimensions include a positive affective dimension, plenitude; a ne...

Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers' sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands' tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.

The reckless pursuit of social, environmental, political and cultural issues and brands may alienate the very customer base, whom they try to impress, especially the millennials. Hence, this study intends to study the perceptions of millennials towards brand activism, so that the findings from the study can help the brand managers to steer their brands into the troubled waters of brand activism. The methodology followed is HTAB (Hypothesize, Test, Action, Business), a popular analysis framework given by Ken Black in his book titled “Business Statistics: Contemporary Decision Making (6th ed.)” A sample comprising of 286 respondents was collected. The final data had 286 observations and 45 features across seven categories. It was found that millennials prefer to buy a brand if it supports a cause or purpose and they stop buying if brand behaves unethically. It was also observed that there is no gender difference amongst the millennials towards their perceptions concerning brand activi...

The rapid of internet and social media users have changed the way people interact in their daily activities. For example, banking and retail began to use various social media, especially online media such as tweeter. The problem that arises is how to get information from thousands and even million data generated through social media, to be a decision as in predicting consumer satisfaction of the service or product.Another problem is the social media users in communicating using slang or local language. In sentiment analysis to predict the sentiment is not easy because it must be able to identify the words. In sentiment analysis, to overcome these problems the method used is text mining so as to process opinions from social media. The proposed approach is to analyze optimal term weighting between TF-IDF, frequency term (TF) and Binary Term Occurrence (BTO), using SVM algorithm. Target feature extraction for selection of datasets by predicting positive and negative sentiments. The result of weighting of terms approaching sentiment is using TF-IDFwith SVM.

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machinelearning algorithms for classification of customers' sentiments. The proposed framework also tested to evaluate the system's performance. Our proposed methodology will be helpful for researchers, service providers, and decision-makers.

With the advent of social media, people have found new ways through which they can express their views, opinions, and beliefs. This study presents an interdisciplinary nature of research where sentiment analysis is applied to the economics discipline of productivity as an experimental study to introduce new service for libraries' users. Firstly, data were retrieved from Twitter on 20 different queries related to productivity using RapidMiner platform and then sentiment analysis was performed employing AYLIEN Text Analysis Software. A total of 6416 tweets were mined from Twitter for a period of 13 days. Further, 676 prominent hashtags had been identified where 83 hashtags were found to be associated with a geographical location in the tweets. It was observed that the United Kingdom was the most popular country which was being used as a hashtag on Twitter in relation to various facets of the productivity followed by India, the United States, China, and Nigeria. In regard to polarity, most of the tweets were found to be of neutral polarity and most of the positive tweets had a low percentage value. The information analyzed using this strategy can be repackaged as a consolidated time-based service and can be presented to libraries' users in different formats. This study not only introduces a nascent way to cater to the information needs of today's users, but also proposes a new way of conducting marketing in libraries using social media mining and sentiment analysis.

The COVID-19 pandemic can be considered as the greatest challenge of our time and is defining and reshaping many aspects of our life such as learning and teaching, especially in the academic year of 2020. While some people could adapt quickly to online learning, others consider it to be inefficient. The reopening of schools and universities is currently under consideration. However, many experts in many countries suggested that at least one semester should be online, during the pandemic. Understanding the public's emotional reaction to online learning has become significant. This paper studies the attitude of people of Saudi Arabia towards online learning. We have used a collection of Arabic tweets posted in 2020, collected mainly via hashtags that originated in Saudi Arabia. Our sentiment analysis has shown that people have maintained a neutral response to online learning. This study will allow scholars and decision makers to understand the emotional effects of online learning on communities.

Recently, Social media has arisen not only as a personal communication media, but also, as a media to communicate opinions about products and services or even political and general events among its users. Due to its widespread and popularity, there is a massive amount of user reviews or opinions produced and shared daily. Twitter is one of the most widely used social media micro blogging sites. Mining user opinions from social media data is not a straight forward task; it can be accomplished in different ways. In this work, an open source approach is presented, throughout which, twitter Microblogs data has been collected, pre-processed, analyzed and visualized using open source tools to perform text mining and sentiment analysis for analyzing user contributed online reviews about two giant retail stores in the UK namely Tesco and Asda stores over Christmas period 2014. Collecting customer opinions can be expensive and time consuming task using conventional methods such as surveys. The sentiment analysis of the customer opinions makes it easier for businesses to understand their competitive value in a changing market and to understand their customer views about their products and services, which also provide an insight into future marketing strategies and decision making policies.

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. ...

Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers' sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands' tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.

The selection of West Java governor is one event that seizes the attention of the public is no exception to social media users. Public opinion on a prospective regional leader can help predict electability and tendency of voters. Data that can be used by the opinion mining process can be obtained from Twitter. Because the data is very varied form and very unstructured, it must be managed and uninformed using data pre-processing techniques into semi-structured data. This semi-structured information is followed by a classification stage to categorize the opinion into negative or positive opinions. The research methodology uses a literature study where the research will examine previous research on a similar topic. The purpose of this study is to find the right architecture to develop it into the application of twitter opinion mining to know public sentiments toward the election of the governor of west java. The result of this research is that Twitter opinion mining is part of text min...

The selection of West Java governor is one event that seizes the attention of the public is no exception to social media users. Public opinion on a prospective regional leader can help predict electability and tendency of voters. Data that can be used by the opinion mining process can be obtained from Twitter. Because the data is very varied form and very unstructured, it must be managed and uninformed using data pre-processing techniques into semi-structured data. This semi-structured information is followed by a classification stage to categorize the opinion into negative or positive opinions. The research methodology uses a literature study where the research will examine previous research on a similar topic. The purpose of this study is to find the right architecture to develop it into the application of twitter opinion mining to know public sentiments toward the election of the governor of west java. The result of this research is that Twitter opinion mining is part of text mining where opinions in Twitter if they want to be classified, must go through the preprocessing text stage first. The preprocessing step required from twitter data is cleansing, case folding, POS Tagging and stemming. The resulting text mining architecture is an architecture that can be used for text mining research with different topics.

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pretrained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.

Sentiment analysis in the finance domain is widely applied by investors and researchers, but most of the work is conducted for English text. In this work, we present a framework to analyze and visualize the sentiments of Arabic tweets related to the Saudi stock market using machine learning methods. For the purpose of training and prediction, Twitter API was used for collecting off-line data, and Apache Kafka was used for real-time streaming tweets. Experiments were conducted using five machine learning classifiers with different feature extraction methods, including word embedding (word2vec) and the traditional BoW methods. The highest accuracy for the sentiment classification of Arabic tweets was 79.08%. This result was achieved with the SVM classifier combined with the TF-IDF feature extraction method. At the end, the predicted sentiments of the tweets using the outperforming classifier were visualized by several techniques. We developed a website to visualize the off-line and streaming tweets in various ways: by sentiments, by stock sectors, and by frequent terms.

This paper presents an approach for author profiling of an unknown users from their texts produced in social media. In particular, we address the identification of two profile dimensions: gender and language variety, of Arabic twitter users based on their tweets. Our approach focused on applying metaclassification technique on features extracted from tweets body. We explored two main sets of features which are character and word n-grams. The proposed approach allowed us to reach promising results for both language variety and gender identification

In this paper, we examine the extent to which adverbs are, in themselves, sentiment-laden, the effect they have on the words they modify as well as the sentiment of sentences they appear in as a whole and consider the sentiment scores as listed from SentiWordNet in relation to definitions laid out in WordNet. We examined 100 adverbs of manner, comparing their definitions as laid out in WordNet with their sentiment scores as given in SentiWordNet. It was concluded that adverbs of manner are in themselves sentiment-laden and that there is a significant-enough disparity between the definitions and the sentiment scores to introduce errors in SentiWordNet-based automated sentiment evaluations of sentences.

Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock ma...

Sentiment analysis is a broad research area in academic as well as business field. The term sentiment refers to the feelings or opinion of the person towards some particular domain. Hence it is also known as opinion mining. It leads to the subjective impressions towards the domain, not facts. It can be expressed in terms of polarity, reviews or previously by thumbs up and down to denote positive and negative sentiments respectively. Sentiments can be analyzed using NLP, statistics or machine learning techniques. Sentiment analysis may ask questions regarding “customer satisfaction and dissatisfaction, “public opinion towards new iPhone series launched” etc. In real world, public or consumer opinions about some product or brand are very important for its sell. Hence sentiment analysis is a very important research area for real life applications i.e. decision making. One of the most visited social networking sites by millions of users is twitter where they share their opinion about va...

Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period may lead to clinical depressions and the long-lasting traits of prevailing depressions can be life threatening with suicidal ideation as the possible outcome. The increasing concern towards the rise in number of suicide cases is because it is one of the leading cause of premature but preventable death. Recent studies have shown that mining social media data has helped in quantifying the suicidal tendency of users at risk. This potential manuscript elucidates the taxonomy of mental healthcare and highlights some recent attempts in examining the potential of quantifying suicidal tendency on social media data. This manuscript presents the classification of heterogeneous features from social media data and handling feature vector represe...

Language Identification is an NLP task which aims at predicting the language of a given text. For the Arabic dialects many attempts have been done to address this topic. In this paper, we present our approach to build a Language Identification system in order to distinguish between Moroccan Colloquial Arabic and Arabic languages using two different methods. The first is rule-based and relies on stop word frequency, while the second is statically-based and uses several machine learning classifiers. Obtained results show that the statistical approach outperforms the rule-based approach. Furthermore, the Support Vector Machines classifier is more accurate than other statistical classifiers. Our goal in this paper is to pave the way toward building advanced Moroccan dialect NLP tools such as morphological analyzer and machine translation system.

Impact of social media networks such as Twitter, Facebook, Instagram, etc. on business is vital since people opinions and attitudes may affect the success or failure of a product or a service. This study is a part of continues research project entitled “Evaluating the influence of Twitter on the Saudi Arabian Stock market (TADAWUL)1 to investigate the impact of Twitter financial tweets on the Saudi Arabia stock market. This paper presented BORSAH an Arabic financial sentiment analysis dataset (corpus) crawled from Twitter. The collected dataset consists of (41,455) Arabic gold-standard annotated Twitter feeds gathered from (118,283) tweets tagged manually from total crawled dataset that consists of (277,453) tweets. The experiment went through three steps, Firstly, we labeled the corpus for Subjectivity and Sentiment Analysis (SSA). Secondly, we applied three machine learning algorithms on part of the corpus. Thirdly, we calculated the accuracy rate of each algorithm. A first sub-co...

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anticounterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users' predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students' studying statuses. Furthermore, the results show matching between students' opinions in CRM and CEM, especially in the negative and neutral classes.

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, "transform case", "tokenize", "token filters by length", "n-gram", "stemming" were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

Efficient Market Hypothesis (EMH), states that at any point in time in a liquid market security prices fully reflect all available information. This paper presents a study of proving the hypothesis through daily Twitter sentiments using the hybrid approach of the lexicon-based approach and the naïve Bayes classifier. In this research we analyze the currency exchange rate movement of Indonesia Rupiah vs US dollar as a way of testing the Efficient Market Hypothesis. In order to find a correlation between the prediction sentiments from Twitter data and the actual currency exchange rate trends we collect Twitter data every day and compute the overall sentiment to label them as positive or negative. Experimental results have shown 69% correct prediction of sentiment analysis and 65.7% correlation with positive sentiments. This implies that EMH is semi-strong Efficient Market Hypothesis, and that public information provide by Twitter sentiment correlate with changes in the exchange market trends.

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, "transform case", "tokenize", "token filters by length", "n-gram", "stemming" were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, "transform case", "tokenize", "token filters by length", "n-gram", "stemming" were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, "transform case", "tokenize", "token filters by length", "n-gram", "stemming" were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

In this paper, Twitter has been chosen as a platform for clustering the topics that have been mentioned by King Abdulaziz University students to understand students' behaviours and answer their inquiries. The aim of the study is to propose a model for clustering analysis of Saudi Arabian (standard and Arabian Gulf dialect) tweets to segment topics included in the students' posts. A combination of the natural language processing (NLP) and the machine learning (ML) method to build models is used to cluster tweets according to their text similarity. K-mean algorithm is utilised with different vector representation schemes such as TF-IDF (term frequencyinverse document frequency) and BTO (binary-term occurrence). Distinct preprocessing is explored to obtain the Ngrams term of tokens. The cluster distance performance task is applied to determine the average between the centroid clusters. Moreover, human evaluation clustering is performed by looking at the data source to make sure that the clusters are making sense to an educational domain. At this moment, each cluster has been identified, and students' accounts on Twitter have been known by their facilities or their educational system, such as elearning. The results show that the best vector's representation was using BTO, and it will be useful to apply it to cluster students' text instead of the TF-IDF scheme.

Text mining methods involve various techniques, such as text categorization, summarisation, information retrieval, document clustering, topic detection, and concept extraction. In addition, because of the difficulties involved in text mining, visualisation techniques can play a paramount role in the analysis and pre-processing of textual data. This paper will present two novel frameworks for the classification and extraction of the association rules and the visualisation of financial Arabic text in order to realize both the general structure and the sentiment within an accumulated corpus. However, mining unstructured data with natural language processing (NLP) and machine learning techniques can be arduous, especially where the Arabic language is concerned, because of limited research in this area. The results show that our frameworks can readily classify Arabic tweets. Furthermore, they can handle many antecedent text association rules for the positive class and the negative class.

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users' predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students' studying statuses. Furthermore, the results show matching between students' opinions in CRM and CEM, especially in the negative and neutral classes.

Product reviews are becoming increasingly useful. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. In addition, Regarding to the comparison between SVM and Bayesian method, we have split the data into two independents subsets form different periods and the experiments were carried out for each subsets respectively in order to distinction between positive and negative examples by using neutral training examples in learning facilitates. Similar result has been given.

This article takes stock of sustainability research in marketing and argues for developing a Strong Sustainability Research (SSR) program, led by a Consumer Culture Theory (CCT) approach. First, I define weak vs. strong sustainability and identify two main problems with continuing to research business with the weak sustainability approach. Second, I discuss past approaches to sustainability research in marketing, which primarily promote weak sustainability. Third, I use the agriculture industry to illustrate how an SSR program in marketing could be developed to bring insights to practitioners and policy makers and build new modes of production, consumption and exchange. Finally, I suggest that the SSR program facilitates collaboration between mainstream marketing and CCT researchers by providing a common ontological platform that can transform epistemological differences into complementary strengths. I argue SSR is a way that marketing research can gain broad impact and relevance.

Abstract - The proliferation of mobile technology, with the privacy and ubiquity that it offers often presents social media pseudo- confidant for lonely and depressed individuals. Social media continues to play an active part in contemporary human life, according to statistics about 40% of the global population are currently active on social media. According to the World Health Organization, at least one in every twenty-five people living as at October 2019 are depressed. Several authors and researchers have developed and proposed methods to predict depression through sentiments in social media posts. Twitter have been widely investigated although other social networks have also been discoursed such as Sina (a Chinese micro blog) and Myspace. It is in light of this that this research seeks to gather social media dataset that is applicable for non-clinical early prediction of the mental health status of users towards strengthening business intelligence. Hence, this work is focusing on mining comments from twitter as a mitigation technique for depression.

The study attempts to map Twitter activity of selected Indian libraries using word frequency and sentiment analysis. Tweets of 18 libraries' (5 academic libraries, 5 government libraries, 5 school libraries and 3 public libraries) were downloaded during June and July 2019. 'R' software was used for the analysis. The study finds that Indian libraries are less active on Twitter. The word cloud based on the most frequently occurring words from the Tweets observed variations in Tweets depend upon the type of libraries. Sentiment analysis of Tweets showed that most of the libraries' Tweets are positive in nature. The study recommends that Indian libraries should use twitter to promote their collections and services.

Emotional branding is related to building a long-term relationship between a product and consumers. Emotional branding is related to someone's experience, about the design of a product that makes you interested in buying (Zyman, in Gobe 2005). The emotional branding approach is a very important element that distinguishes success between the obscurity of brands on the market. Nowadays many companies have tried to understand the art of assessing ingenuity and sensitivity which is the true strength of human emotions (Gobe 2005). Emotional branding brings new brand credibility and personality by building strong relationships with the community in a personal and Holistic. It means based on unique beliefs built with the audience, emotional branding increases sales on the basis of the need to fulfill customer desires. A customer's commitment to a product or institution created from the experience they feel directly about a brand. For example, the pride of a customer when he gets a gift from a brand, or get a positive experience shopping in a pleasant situation such as known by the shopkeeper, or suddenly brought a cup of coffee while waiting that all pleasant feelings who consumer gets, its the core of emotional branding. Emotional branding is not just a research process or technique, it is based on relationships between people that are more than data and graphics. This is a culture and a way of life for the fundamental belief that human beings are the real power in commerce and that business and path cannot be separated. Branson say is that what we call "shareholder value" is best determined by how strongly the employee feels and the customer feels tied to your brand. All seems clearer to me that a product or service is only a brand when there are deep values that can be interpreted into the facts and feelings that employees can represent and customers get comfort. In-depth, his mean simple. Everyone values being treated well. Everyone respects honesty. Everyone craves excellence and value. Everyone likes excitement and feels a piece of something bigger than himself. the importance of this forms a rather simple view of his business, that's a number of factors supporting emotional branding (Branson in Travis 2000) Big question about "how does your customer feel about your brand?" not an ordinary question. This is a crucial question. this is not only important for the marketing and sales department. but all stakeholders must have a very important interest in understanding customer feelings. It is the hard task of a company related to the feelings that audiences feel so that the

Using actor-network theory from sociology, the author explores the creation of new markets as a brand-mediated legitimation process. Findings from an eight-year longitudinal investigation of the Botox Cosmetic brand suggest that the meanings of a new cosmetic self-enhancement technology evolve over the course of contestations between brand images promoted by the innovator and doppelgänger brand images promoted by other stakeholders. Each contestation addresses an enduring contradiction between nature and technology. A four-step brand image revitalization process is offered that can be applied either by managers interested in fostering an innovation's congruence with prevailing social norms and ideals or by other stakeholders (e.g., activists, competitors) interested in undermining its marketing success. The findings integrate previously disparate research streams on branding and market creation and provide managers with the conceptual tools for sustaining a branded innovation's legitimacy over time.

Using actor-network theory from sociology, the author explores the creation of new markets as a brand-mediated legitimation process. Findings from an eight-year longitudinal investigation of the Botox Cosmetic brand suggest that the meanings of a new cosmetic self-enhancement technology evolve over the course of contestations between brand images promoted by the innovator and doppelgänger brand images promoted by other stakeholders. Each contestation addresses an enduring contradiction between nature and technology. A four-step brand image revitalization process is offered that can be applied either by managers interested in fostering an innovation's congruence with prevailing social norms and ideals or by other stakeholders (e.g., activists, competitors) interested in undermining its marketing success. The findings integrate previously disparate research streams on branding and market creation and provide managers with the conceptual tools for sustaining a branded innovation's legitimacy over time.

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anticounterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

In this paper we have explained the detailed work done in developing a system which can be used for the purpose of opinion analysis of a product or a service. The system readily processes the tweets by pulling data from tweeter posts, preprocessing it and connecting to Alchemy API by REST call method. and showing the result graphically. We have given the analysis for the product Samsung Galaxy Our proposed system access the public tweets by API and filters them for Samsung Galaxy .The analysis is being carried out as to classify the sentiment as positive, negative or neutral.