scholarly journals SARCASM ANALYSIS AND MOOD RETENTION USING NLP TECHNIQUES

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Social Media ◽  
Facial Expression ◽  
Sentiment Analysis ◽  
Service Evaluation ◽  
Research Areas ◽  
Written Texts ◽  
The Given ◽  
Proper Interpretation

Sarcasm detection in written texts is the Achilles’ heel of research areas in sentiment analysis, especially with the absence of the rightful verbal tone, facial expression or body gesture that leads to random misinterpretations. It is crucial in sectors of social media, advertisements and user feedbacks on services that require proper interpretation for service evaluation and improvisation of their products. The objective here thereby is to identify sarcasm within a given text by experimenting with the original predicted mood of the text and work on its transformation with the several variations in combination of the standard sarcastic elements present in the corresponding writing. Here standard NLP techniques are used for identification and interpretation. This involves detecting primary connotation of the given text (e.g. positive/neutral/negative), followed by detecting elements of sarcasm. Then, under the presence of the sarcasm indicator algorithm, the rightful interpretation of the previously detected mood is attempted.

scholarly journals Sentiment Analysis on New Currency in Kenya using Twitter Dataset

2020 ◽  
Vol 8 (2) ◽  
pp. 81
Author(s):  
Ibrahim Moge Noor ◽  
Metin Turan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cross Validation ◽  
Naive Bayes ◽  
Major Influence ◽  
Research Areas ◽  
Average Accuracy ◽  
Bayes Algorithm ◽  
Insight Into ◽  
Unbiased Prediction

Social media sites recently became popular, it is clear that it has major influence in society. Twitter is one of these sites, full of people’s opinions, where one can truck sentiment express about different kinds of topics. Sentiment analysis is one of the major interesting research areas nowadays. In this paper, we focused on Sentimental insight into the 2019 Kenya currency replacement. Kenyans citizens expressed their reaction over new banknotes. We perform sentiment analysis of the tweets from twitter using the Multinomial Naïve Bayes algorithm. We split our dataset using k-folder cross validation since we had limited amounts of data, so to achieve unbiased prediction of the model we obtained an average accuracy of 75.3%.

scholarly journals Hadoop Based Generic Template for Performing Sentiment Analysis Using Apache PIG

2021 ◽  
Vol 9 (VII) ◽  
pp. 3667-3774
Author(s):  
Gadige Vishal Sai
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Streaming Data ◽  
Social Media Platforms ◽  
Social Media Monitoring ◽  
Data Tracking ◽  
Media Monitoring ◽  
Apache Pig ◽  
The Given ◽  
Transactional Data

Every day over 2.5 quintillion data is generated using various channels like online surveys, transactional data tracking, social media monitoring, etc. Out of these majority of the data is generated using social media platforms. This raw data contains information that can be used for industrial, economic, social and business purposes. To facilitate this, sentiment analysis has become a prospect for various tech-based industry giants to review and analyze their products. Hadoop has been established as one of the best tools for storing, processing, and streaming data in the market. In this paper, we present a generic approach to performing sentiment analysis using Apache PIG which classifies the given data taken from a dataset to either positive or negative to get the people’s sentiment over an object or an issue.

scholarly journals Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication

2020 ◽  
Vol 10 (2) ◽  
pp. 431
Author(s):  
Fabian Wunderlich ◽  
Daniel Memmert
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Public Interest ◽  
Online Communication ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Sports Science ◽  
Sports Events ◽  
Research Areas ◽  
Textual Data

Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football matches are accompanied by a huge public interest and large amount of related online communication, social media analysis in general and sentiment analysis in particular are almost unused tools in sports science so far. The present study tests the feasibility of lexicon-based tools of sentiment analysis with regard to football-related textual data on the microblogging platform Twitter. The sentiment of a total of 10,000 tweets with reference to ten top-level football matches was analyzed both manually by human annotators and algorithmically by means of publicly available sentiment analysis tools. Results show that the general sentiment of realistic sets (1000 tweets with a proportion of 60% having the same polarity) can be classified correctly with more than 95% accuracy. The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science.

The features of transnational corporations’ advertisement in social networks

2019 ◽  
pp. 89-92
Author(s):  
Daniella Mushka ◽  
Yeva Erfan
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Wide Spectrum ◽  
Subsequent Development ◽  
Transnational Corporations ◽  
Target Audience ◽  
Social Media Marketing ◽  
Scientific Article ◽  
The Social ◽  
The Given

This scientific article considers all aspects, modern importance and growing role of the social media marketing and advertisement in the general spectrum of marketing activity for developed and developing brands. Investigational actuality and basic directions of application of all spectrum of instruments of social networks for the sake of advancement of product and the processes of forming perception of trade mark and forming the image of brand are analyzed by the authors of the article. The given scientific article highlights the most popular trends and patterns of goods and trademarks’ promotion in the world in the context of updating the concept of advertising on social networks. The bigger and more engaged your target audience is on social media networks (Instagram, Facebook, Twitter, YouTube etc), the easier it will be for you to achieve every other marketing or business goal. The importance of social media marketing’s assistance in attracting new potential clients and customers to the company is also considered in the given article. Besides that, the authors of the article list and analyse wide spectrum of basic trends considering promotion and advertising in 2019 among the well-known brands. In addition to this all, the list of the most successful publicity advertisement campaigns of this year and brands which were promoted with their assistance are listed and analysed. In the context of the study, it shows up that advertising campaigns play a significant role not only in reaching sales but also in generating overall customer loyalty to the brand. This makes it possible to argue that the most reputable brands should have an important social goal that will be positively accepted by society and target audience in addition to the high quality and usability of the products or services. Social networking is the easiest way to see the social response to your promotion and lead to an instant purchase. Therefore, relying on the experience of the already well-known multinational and transnational corporations, social media marketing should take a significant share of the overall promotion of the company. The connection between the brand and potential customer should be built on the emotions that accompany consumers when viewing ads and using products. This scientific article eventually declares conclusions and prognoses in relation to subsequent development of these instruments and platforms for advancement and branding of small and large enterprises in future. It states that emotional connection between person and brand is much more effective for the company than an expensive ad.

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

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