Classifying Business Marketing Messages on Facebook (original) (raw)
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2016
Facebook is one of the most used socials networking sites. It is more than a simple website, but a popular tool of communication. Social networking users communicate between them exchanging a several kinds of content including a free text, image and video. Today, the social media users have a special way to express themselves. They create a new language known as "internet slang", which crosses the same meaning using different lexical units. This unstructured text has its own specific characteristics, such as, massive, noisy and dynamic, while it requires novel preprocessing methods adapted to those characteristics in order to ease and make the process of the classification algorithms effective. Most of previous works about social media text classification eliminate Stopwords and classify posts based on their topic (e.g. politics, sport, art, etc). In this paper, we propose to classify them in a lower level into diverse pre-chosen classes using three machine learning algorithms SVM, Naïve Bayes and K-NN. To improve our classification, we propose a new preprocessing approach based on the Stopwords, Internet slang and other specific lexical units. Finally, we compared between all results for each classifier, then between classifiers results.
A Review: Text Classification on Social Media Data
In today's world most of us depend on Social Media to communicate, express our feelings and share information with our friends. Social Media is the medium where now a day's people feel free to express their emotions. Social Media collects the data in structured and unstructured, formal and informal data as users do not care about the spellings and accurate grammatical construction of a sentence while communicating with each other using different social networking websites (Facebook, Twitter, LinkedIn and YouTube). Gathered data contains sentiments and opinion of users which will be processed using data mining techniques and analyzed for achieving the meaningful information from it. Using Social media data we can classify the type of users by analysis of their posted data on the social web sites. Machine learning algorithms are used for text classification which will extract meaningful data from these websites. Here, in this paper we will discuss the different types of classifiers and their advantages and disadvantages.
The influence of text classification on facebook with AISAS method
International Journal of Business Information Systems, 2020
Social media marketing has never been closely tied like it is now. Online marketing managers thus need to actively analyse and understand this fact beyond sentiments of the visitors of their webs. The objective is to comprehend emotions, feelings towards their threads and participation. This research aims to study the text classification that is used to analyse Dentsu's AISAS patterns of posts in order to understand clients' thinking. The study applies naïve Bayesian as a component in text mining technique together with adoption of Dentsu's AISAS model as a framework. Naïve Bayesian methodology is also used to classify attention, interest, search, action and share for analysis understanding. We hope that this study will help online marketers in responding and adjusting each online campaign with the proper strategy.
International Journal of Research in Marketing
The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then-based on a categorization of seven different types of eWOM (e.g., question, complaint)-classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.
Enhancement of Social Media Text Classification
In this information age, social media is a powerful online communication tool for people to present their expression such as the real-time events report, personal information including their emotions. Social media text significantly demonstrate information in a current society, also indicate a trend of the dynamic social movement. However, these text characteristics are in a form of informal and unstructured language, i.e. using abbreviation, short text message, slang and argot. It is difficult to classify and extract the key information. There are many techniques proposed to categorize this kind of text information. The social media text classification by using Term Frequency-Inverse Document Frequency (TF-IDF) weighting with Word Article Matrix (WAM) and initial keywords from the well-formed source, i.e. online news article, is one of a productive technique. It generates a set of the sufficient keyword terms to represent text categories. In this paper, we discuss about the proper iteration number of the WAM updating process which generate the most effective word vector matrix that can classify the social media text effectively.
Effectiveness of Social Media Text Classification by Utilizing the Online News Category
Social media text can illustrate significant information of our real social situation. It can show the direction of real-time social movement. However, it has its own characteristics such as using short text and informal language, many unstructured information and argot. This kind of text is hard to classify and difficult to analyze to extract the useful information. In this paper, we propose an effective technique to classify the social media text by utilizing the initial keywords from well-formed sources of data, such as online news. Term frequency–inverse document frequency weighting technique (TF-IDF) and Word Article Matrix (WAM) are used as main methods in this research. We use the extracted keywords from the well-formed source as a main factor to do experiment on Twitter messages. We found a set of the social media keywords can represent the essence of social events and can be used to classify the text effectively.
Personalizing marketing messages for specific audience segments is vital for increasing user engagement with advertisements, but it becomes very resource-intensive when the marketer has to deal with multiple segments, products or campaigns. In this research, we take the first steps towards automating message personalization by algorithmically inserting adjectives and adverbs that have been found to evoke positive sentiment in specific audience segments, into basic versions of ad messages. First, we build language models representative of linguistic styles from user-generated textual content on social media for each segment. Next, we mine product-specific adjectives and adverbs from content associated with positive sentiment. Finally, we insert extracted words into the basic version using the language models to enrich the message for each target segment, after statistically checking in-context readability. Decreased cross-entropy values from the basic to the transformed messages show that we are able to approach the linguistic style of the target segments. Crowdsourced experiments verify that our personalized messages are almost indistinguishable from similar human compositions. Social network data processed for this research has been made publicly available for community use.
A machine learning-based approach to enhancing social media marketing
Computers & Electrical Engineering, 2020
Social media (SM) represent beneficial channels for marketers, business promoters and consumers. To acquire continuous revenues and more active customers, key business players should understand the behaviour and purchase preferences of buyers. To predict the buying decisions of purchasers, data about purchase intentions and desires have to be extracted with the help of data mining techniques. The purpose of this paper is to examine social media data analytics using machine learning tools; this new approach for developing a social media marketing strategy employs the Waikato Environment for Knowledge Analysis (WEKA). WEKA is compared with other algorithms of interest and found to outperform its peers, especially with regard to parameters such as precision, recall, and F-measure, indicating that WEKA performs better than other approaches.
ISAAC YAKUBU, 2023
SVM have proven to be highly effective in handling high-dimensional and complex data, making them an ideal choice for text classification. They excel at finding optimal decision boundaries and capturing intricate patterns within data, resulting in accurate and reliable predictions. In this paper, I will leverage these attributes to develop an AI model that classifies text data into distinct niches, contributing to enhanced targeting and marketing strategies within the advertising ecosystem of social media platforms. The significance of this research lies in its potential to revolutionize social media advertising strategies. By accurately classifying the niche of textual content on platforms like Twitter, advertisers can tailor their campaigns to specific user preferences and interests. This fine-tuned targeting can lead to improved engagement, higher conversion rates, and more efficient resource allocation. Additionally, the model's insights can assist in sentiment analysis and trend prediction, enabling advertisers to stay ahead in a dynamic online landscape. To validate our proposed SVM-based niche classifying AI model, I conduct comprehensive experiments on a substantial dataset of public Twitter data. Through rigorous testing and evaluation, I demonstrate the model's capability to effectively categorize niche categories. The outcomes underscore the robustness of SVMs in handling text classification tasks, particularly in the context of niche analysis for social media advertising systems. In conclusion, this paper bridges the gap between SVMs, text classification, and social media advertising. By leveraging SVMs for niche categorization within the advertising system of social media platforms, I propose a model that can significantly enhance targeting precision, campaign effectiveness, and audience engagement. As the digital landscape continues to evolve, our SVM-based model offers a promising avenue for refining advertising strategies in the dynamic realm of social media.