Sentiment Analysis for Promotional Campaigns (original) (raw)
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Natural Language Processing for Sentiment Analysis in Social Media Marketing
Organizations often use sentiment analysis-based systems or even resort to simple manual analysis to try to extract useful meaning from their customers' general digital "chatter". Driven by the need for a more accurate way to qualitatively extract valuable product and brand-oriented consumer-generated texts, this paper experimentally tests the ability of an NLP-based analytics approach to extract information from highly unstructured texts. The results show that natural language processing outperforms sentiment analysis for detecting issues from social media data. Surprisingly, the experiment shows that sentiment analysis is not only better than manual analysis of social media data for the goal of supporting organizational decision-making, but may also be disadvantageous for such efforts.
Sentiment Analysis on Social Media
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012
The Web is a huge virtual space where to express and share individual opinions, influencing any aspect of life, with implications for marketing and communication alike. Social Media are influencing consumers' preferences by shaping their attitudes and behaviors. Monitoring the Social Media activities is a good way to measure customers' loyalty, keeping a track on their sentiment towards brands or products. Social Media are the next logical marketing arena. Currently, Facebook dominates the digital marketing space, followed closely by Twitter. This paper describes a Sentiment Analysis study performed on over than 1000 Facebook posts about newscasts, comparing the sentiment for Rai -the Italian public broadcasting service -towards the emerging and more dynamic private company La7. This study maps study results with observations made by the Osservatorio di Pavia, which is an Italian institute of research specialized in media analysis at theoretical and empirical level, engaged in the analysis of political communication in the mass media. This study takes also in account the data provided by Auditel regarding newscast audience, correlating the analysis of Social Media, of Facebook in particular, with measurable data, available to public domain.
Sentiment analysis – an overview of a technique which can be used in marketing activities
2019
A sentiment analysis, a form of artificial intelligence, is a technique which uses natural language processing (NLP) to ascertain the opinions and emotional tone of the user written content on the online platform. It can be used in any form ranging from determining the sentiments of consumer’s reviews, employee’s feedback, and their social presence for effective marketing of their products and services. Through this article we wish to analyse the existing literature in sentiment analysis field to ascertain it usefulness in the marketing activities
IRJET, 2022
Nowadays, social media is getting more attention. Public and private opinions on a wide range of topics are constantly expressed and distributed via a variety of social media platforms. Twitter is one of the most prominent social networking platforms. Twitter provides businesses with a quick and effective approach to assess customers' viewpoints on issues that are crucial to market success. Creating a sentiment analysis programme is a method for computing consumer perceptions. This study describes the creation of a sentiment analysis that extracts a large number of tweets. Tweepy, numpy, pandas, textblob, and nltk are some of the Python modules utilised in this project. Results classify customers' perspective via tweets into positive and negative, which is represented in a pie chart and tabular form.
Sentiment Analysis on Twitter Data: A Comparative Approach
International Journal of Computer Science and Mobile Applications, 2021
Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), N...
Sentiment Analysis for Social Media
Applied Sciences
Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.
Application for sentiment and demographic analysis processes on social media
Global Journal of Computer Sciences: Theory and Research, 2018
Consumers used to make their complaints via phone and mail before the concept of social media was developed. Now, consumers have begun to state their wishes and complaints concerning companies using social media, and the firms’ adaptation to social media has been increased. Understanding the negative and positive attitudes of customers towards ads and questions has gained utmost importance for the companies’ decisions to be made for the future. Today, companies are able to continue their existence in the modern world’s competitive environment by adjusting their advertising and marketing strategies and calculating their budgets to social media analyses they get. In addition, companies depend on those analyses in order to determine their positions in the market and create their action plans. In this study, instant messages sent on social media and demographic information were used in the data analysis in order to determine whether those messages included positive or negative attitudes...
Handbook of Research on Pattern Engineering System Development for Big Data Analytics, 2018
E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.
Social Media Sentiment Analysis
2014
During the last years, the popularity of microblogging and social media services such as Twitter has increased significantly. Lots of users often use these services to express feelings or opinions about a variety of subjects. The analysis of this kind of content can extract useful information for fields such as personalized marketing or social profil-ing. In addition, it can help consumers decide whether to buy or not a certain product. However such a task is not trivial, because the language used in Social media is often informal presenting new challenges to text analysis. In this thesis we describe a sys-tem that was developed to detect sentiment in microblogging content such as Tweets or SMS messages in English. Our system has competed in two international challenges and has achieved very good results. We also apply our methodology to create a system for Greek. Finally, we propose ideas for future work. ii Acknowledgements I would like to thank my supervisor, Ion Androutsopoulos,...