Twitter Sentiment Analysis

Social media continues to gain increased presence and importance in society. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining increased popular. Twitter offers organizations a fast and effective way to analyze customers‟ perspectives toward the critical to success in the marketplace. Developing a program for sentiment analysis is an approach to be used to computationally measure customers‟ perceptions. This paper reports on the design of a sentiment analysis extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers‟ perspective via tweets into positive and negative which is represented in pie chart and html page. However, the program has planned to develop on web application system but due to limitation of Django which can be worked on Linux server or LAMP, for further this approach need to be done

Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.

In past two decades e-commerce platform developed exponentially, and with this advent, there came several challenges due to a vast amount of information. Customers not only buy products online but also get valuable information about a product they intend to buy through an online platform. Customers share their experiences by providing feedback which creates a pool of textual information and this process continuously generates data every day. The information provided by customers contains both subjective and objective text that contains a rich information regarding behaviour, liking and disliking towards a product and sentiments of customers. Moreover, this information can be helpful for the customers who are yet to buy or who are yet in decision making process. This thesis studies comparison of four supervised machine learning approaches to predict customer satisfaction. These approaches are: Naïve Bayes, Support Vector Machines (SVM), Logistic Regression (LR), and Decision Tree (DT). The models use term frequency inverse document frequency (TF-IDF) vectorization for training and testing sets of data. The models are applied after basic pre-processing of text data that includes the lower casing, lemmatization, the stop words removal, smileys removal, and digits removal. We compare the performance of models using accuracy, precision, recall, and F1-scores. Support Vector Machines (SVM) outperforms the rest of the models with the accuracy rate 83% while Naïve Bayes, Logistic Regression (LR) and Decision Tree (DT) have accuracy rate 82%, 78%, and 76%, respectively. Moreover, we evaluate the performance of classifiers using confusion matrix

Deductions from a Sub-Saharan African bank’s tweets: A sentiment analysis approach

The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. © 2020, © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.Tomas Bata University in Zlin [IGA/CebiaTech/2020/001

An Evaluation of Geotagged Twitter Data during Hurricane Irma using Sentiment Analysis and Topic Modeling for Disaster Resilience

Disasters require quick response times, thought-out preparations, overall community, and government support. These efforts will ensure prevention of loss of life and reduce possible damages. The United States has been battered by multiple major hurricanes in the recent years and multiple avenues of disaster response efforts were being tested. Hurricane Irma can be recognized as the most popular hurricane in terms of social media attention. Irma made landfall in Florida as a Category 4 storm and preparation measures taken were intensive thus providing a good measure to evaluate in terms of efficacy. The effectiveness of the response methods utilized are evaluated using Twitter data that was collected from September 1st to September 16th, 2017. About 221,598 geotagged tweets were analyzed using sentiment analysis, text visualization, and exploratory analysis. The objective of this research is to establish an observable pattern regarding sentiment trends over the progression of the storm and produce a viable set of topic models for its totality. The study contributed to the literature by identifying which topics and keywords were most frequently used in tweets through sentiment analysis and topic modeling to determine what resources or concerns were most significant within a region during the hurricane Irma. The results from this study demonstrate that the sentiment analysis can measure people’s emotions during the natural disaster, which the authorities can use to limit the damage and effectively recover from the disaster. In this work, we have also reviewed the related works from the text/sentiment analysis, social media analysis from hurricanes/disaster perspective. This research can be further improved by incorporating sentiment analysis methods for classifying emoticons and non-textual components such as videos or images

Supporting argumentation dialogues in group decision support systems: an approach based on dynamic clustering

Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect-based sentiment analysis (ABSA) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.This research was funded by National Funds through the Portuguese FCT-Fundacao para a Ciencia e a Tecnologia under the R&D Units Project Scope UIDB/00319/2020, UIDB/00760/2020, UIDP/00760/2020, and by the Luis Conceicao Ph.D. Grant with the reference SFRH/BD/137150/2018

Twitter Sentiment Analysis

Social media continues to gain increased presence and importance in society. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining increased popular. Twitter offers organizations a fast and effective way to analyze customers‟ perspectives toward the critical to success in the marketplace. Developing a program for sentiment analysis is an approach to be used to computationally measure customers‟ perceptions. This paper reports on the design of a sentiment analysis extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers‟ perspective via tweets into positive and negative which is represented in pie chart and html page. However, the program has planned to develop on web application system but due to limitation of Django which can be worked on Linux server or LAMP, for further this approach need to be done

Detecting consumer emotions on social networking websites

The social networking environment goes beyond connecting friends. It also connects customers with companies and vice versa. Customers share their experience with friends, followers, and companies and these experiences carry sentiments and emotions thereby creating big data. There is an ocean of data that is available for companies to extract and make meaning out of it by applying to different business contexts such as consumer feedback analysis and marketing & communications. For companies to benefit from consumer emotion data, they must make use of computational methods that can save time and work consumed by traditional consumer research methods such as questionnaires and interviews. The objective of this research is to explore existing literatures on detecting consumer emotions from social networking data. The author carried out a systematic literature review on research articles from three bibliographic databases with the intent to find out social networking data extraction process, dataset sizes, computational methods used, consumer sentiments, emotions studied, limitations and its application in a managerial context. To further understand consumer emotion detection, a case study in the form of a Twitter marketing campaign was conducted to emulate the process of consumer emotion detection on a company that is selling stress management products and services. The results indicate that most companies use Twitter networking platform to carry out consumer emotion analysis. The dataset sizes range from small to very large. The studies have used variety of computational methods, some with accuracies to measure the performance. These methods have been applied in various industries such as travel, restaurant, healthcare, and finance to name a few. Managerial applications include marketing, supply chain, feedback analysis, product development, and customer satisfaction. There are few limitations that were identified from using these methods. The case study results and discussion with the case company CIO communicated the potential for the use of some of the methods for consumer behavior research. The valuable feedback from the CIO revealed that by customizing existing methods, their company can create new tools and methods to understand their customers by providing better recommendations and customize their offerings to individual customers

A new cognition-based chat system for avatar agents in virtual space

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