E-commerce Recommender System on Twitter using Directed Multilayer Network

E-commerce Recommender System on Twitter using Directed Multilayer Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article E-commerce Recommender System on Twitter using Directed Multilayer Network AKSHAY RAJ K P, RINU RAJ T R, JOEL JOY, RINI T PAUL This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4223941/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An E-commerce recommender system analyzes user behavior and preferences to suggest personalized product rec- ommendations, enhancing the shopping experience and boosting sales by presenting items likely to interest individual users. Ecommerce recommender system is integrating a directed mul- tilayer network analysis to optimize product recommendations on Twitter. Our methodology involves modeling user interac- tions as a weighted, directed network through Social Network Analysis, providing insights into influential users and dynamic group dynamics. The directed multilayer network approach is implemented by connecting users (one layer) to the conversations and topics (a second layer) they engage in, with inter-layer con- nections reflecting user participation in conversations. Building upon this, we introduce an innovative e-commerce recommenda- tion system that triggers personalized product suggestions to a user’s friends based on their purchase history. This integration harnesses the wealth of self-reported information on Twitter, delivering targeted and socially-informed e-commerce recommen- dations. The study establishes a robust framework, marrying social network insights with an e-commerce recommender system for effective and personalized product promotion within online communities DMN NCF Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 I. INTRODUCTION In the world of online shopping, making customers happy and boosting sales is crucial. Personalized suggestions have been a big help in this, but imagine if we could go beyond the usual ways and use social media, like Twitter, for insights. Here, we can use a smart approach called directed multilayer networks to really understand what users like. This system doesn’t just look at what people buy; it also checks out their tweets, retweets, follows, and hashtag conversations. By putting all these interactions together, we can carefully figure out what users prefer. In this complex world, products become like connected dots, linked by what users talk about in tweets and retweets. Brands and hashtags become clues to finding out what users really want. This directed multilayer network isn’t just a flat map; it’s a living system that changes as users interact, creating new connections that reflect their changing preferences. This way, we’re not stuck looking at old snapshots of what users bought before. We can predict what they might want next and suggest products that match their evolving interests. This isn’t just a theory; it’s the future of online shopping, where suggestions come naturally from users’ online lives. Every tweet and hashtag becomes a hint, guiding us to the perfect product users didn’t even know they wanted. So, get ready to explore this world of directed multilayer networks and change the way we do online shopping. The journey to build an online shopping suggestion system on Twitter using directed multilayer networks is a big goal, but it’s definitely doable. To make this happen, we need to mix technical skills, data know-how, and a good understanding of what users like. The potential benefits are clear – a system that not only knows what users want but also helps build loyalty to brands, setting the stage for success in the ever- changing digital world. Directed Multilayer Networks (DMNs) aren’t just tools; they’re a way to see the world differently – a way that uncovers hidden patterns and taps into the vast potential of connections. As the world gets more complicated, these networks are the key to figuring out how people behave, predicting trends, and shaping the future. Every whisper, every retweet, and every connection in these networks adds to our understanding, which keeps growing. In the complex world of online shopping, using directed multilayer networks doesn’t just boost technical abilities – it’s a chance to understand how people interact. This cool approach gives us a wide view of what users do, going beyond the usual recommendation systems. The mix of purchases and social media interactions helps us really understand customers, making online shopping more personal. As the directed multi- layer network keeps changing with the ups and downs of what users like, it becomes proof of how technology and human understanding work together. This dynamic combo changes the idea of online shopping, turning it into a detailed experience where every tweet, retweet, and hashtag is like an important part of what customers want. The future of online shopping isn’t just about buying things; it’s a story shaped by online talks and the links in a directed multilayer network II. PROPOSED MODEL The proposed model basically has two layers - one is user- product layer and other is product-product layer. This project basically consist of analyzing user’s tweet which are based on a particular product and recommending similar products based on that.Initially, tweets referencing specific products are collected from Twitter using the Tweepy API. These tweets undergo rigorous text preprocessing to remove irrelevant con- tent such as retweets, links, hashtags, and mentions. Following this, sentiment analysis is performed using either TextBlob or Sklearn tools to classify each tweet as positive, negative, or neutral, based on its polarity. The cleaned and sentiment- labeled tweet data is then transformed into numerical vectors using TF-IDF vectorization. By calculating cosine similarity scores between these vectors, the model establishes connec- tions between tweets expressing positive sentiment towards products, identifying similar products based on their similarity scores. To enhance the model’s performance and ensure robustness, cross-validation techniques are employed, involving the divi- sion of the dataset into training and testing sets. Hyperpa- rameter tuning is conducted using methods like grid search or randomized search to optimize the model’s parameters, including vectorizer settings and similarity thresholds. Once trained and optimized, the model is exported for deployment in production environments, facilitating seamless integration into recommendation systems. Moreover, the model’s versatility extends to the integra- tion of an IMDB recommendation system, where the SVC model and TF-IDF vectorizer trained on the IMDB dataset are imported. By leveraging cosine similarity scores between movie mentions in tweets and movies in the IMDB dataset, the model generates movie recommendations alongside product recommendations. These recommendations are then presented to users, offering a holistic and tailored approach to product and movie suggestions based on sentiment analysis results and similarity scores. Overall, the proposed model represents a sophisticated framework for extracting insights from user- generated content on social media and delivering personalized recommendations to users. A. Data Acquisition The proposed model aims to recommend similar products based on sentiment analysis of tweets mentioning specific products. Tweets are collected and preprocessed to remove noise like retweets and hashtags. Sentiment analysis is con- ducted to classify tweets as positive, negative, or neutral, and TF-IDF vectorization is applied to convert the text data into numerical vectors. Cosine similarity scores between these vectors are then calculated to identify connections between tweets expressing positive sentiment towards products, thus recommending similar products. Cross-validation techniques ensure model robustness, and hyperparameter tuning optimizes performance. The model’s versatility extends to integrating an IMDB recommendation system, leveraging cosine similarity scores between movie mentions in tweets and movies in the IMDB dataset to offer personalized recommendations. Overall, the model provides a sophisticated framework for extracting insights from user-generated content on social media and delivering tailored product and movie suggestions based on sentiment analysis and similarity scores. B. Data preprocessing In the proposed model, data preprocessing is pivotal for preparing the collected tweet data for subsequent analysis. Initially, tweets mentioning specific products are gathered from Twitter. However, these tweets frequently contain noise and extraneous content such as retweets, links, hashtags, and mentions. Therefore, a thorough process of text preprocessing is undertaken to cleanse the data. The preprocessing procedure encompasses several essential steps. Firstly, noise removal is executed to eliminate irrelevant elements from the tweet text, focusing solely on the pertinent content related to the products of interest. Following this, tokenization divides the cleaned tweet text into individual tokens or words, facilitating further analysis by breaking down the text into manageable units. Normalization is then applied to the text data to ensure con- sistency, often involving converting text to lowercase to avoid duplicate representations of words with different cases. Ad- ditionally, stopwords—commonly occurring words that lack significant meaning—are removed to reduce noise and enhance the efficiency of downstream tasks. Further steps include stemming or lemmatization, which reduces words to their root form, aiding in consolidating different variations of the same word and thereby minimizing vocabulary size and enhancing model performance. Special at- tention is paid to emoticons and abbreviations commonly used in social media text to ensure their meaningful representation in the data. Lastly, handling special characters and encoding techniques are applied to ensure compatibility with subsequent analysis and modeling steps. By meticulously conducting these preprocessing steps, the tweet data is cleansed and structured into a suitable format for sentiment analysis and vectorization. This ensures that subsequent analyses are based on high- quality, relevant data, ultimately leading to more accurate recommendations for users. C. Twitter Sentiment Analysis Twitter has become very popular since the release in 2006 and is a social media platform where people can easily put micro blogs, the so-called tweets, on their online Twitter profile. In doing so it provides to be a great source for textual data and personal opinions about all kinds of different topics. The sentiment analyses of these tweets can be considered as a specific topic within the area of sentiment analysis (Zimbra et al. 2018). Various research has been conducted where sentiment analyses are performed on tweets. Asur and Huberman (2010) were able to find how the use of sentiment analysis on tweets can create more accurate forecasts. The insights gained with these Twitter sentiment analyses can lead to interesting outcomes that can be used in areas such as marketing or social studies (Zhang et al. 2011). With Twitter sentiment analysis several challenges come into play. One of these challenges is the use of emoticons in tweets. Lexicon- based approaches are not always able to detect emoticons and they used to get removed from the text (Hogenboom et al. 2013). Research conducted by Hogenboom et al. (2013) found a significant increase in accuracy when emoticons are taken into account. Not only emoticons are used in tweets but also abbreviations, such as acronyms and initialisms, are very common in tweets since a tweet cannot exceed the limit of 280 characters. In earlier research by Kouloumpis, Wilson, and Moore (2011) it was proven useful to make use of emoticons and abbreviations when sentiment analysis is performed on tweets. Therefore, emoticons and abbreviations should both be taken into account when analysing the sentiment of a tweet. In addition to abbreviations and emoticons tweets can also contain slang. Singh and Kumari (2016) found that the usage of slang words in sentiment analysis leads to a better sentiment classification. Thus it is important that slang within tweets should be taken into consideration when a Twitter sentiment analysis is performed. Various sentiment analysis methods can be performed to find the sentiment polarity score of a certain document, text or sentence. In this study both TextBlob and VADER are used for this task. In the subsections below these methods will be clarified. TextBlob : TextBlob can be classified as a lexicon-based approach when used for sentiment analysis. Next to sentiment analysis, Textblob can be used to perform numerous natural language processing tasks which include translation, noun phrase extraction, partof-speech tagging, spelling correction, word and phrase frequencies, and more. Two different sen- timent analysis methods can be utilized by TextBlob. The default method is based on the pattern library and the other method is a Naive Bayes analyzer which has its roots in NLTK (Loria 2018). This study uses the former of the two. The default sentiment analysis function returns a polarity score between − 1.0 and 1. Numbers above zero can be seen as positive, at or close to zero as neutral, and below zero as negative. D. Collaborative filtering Within recommendation systems, collaborative filtering serves as a method to predict or recommend items for users, drawing insights from the preferences of comparable users. In the envisioned model, this technique is implicitly employed within the product-product layer. When calculating cosine similarity scores between the nu- merical vectors representing tweets expressing positive senti- ment towards products, the model is essentially measuring the similarity between different products based on how often they are mentioned together in positive tweets. This is a form of item-based collaborative filtering, where the model identifies products that tend to be liked or recommended by users who have expressed positive sentiment towards similar products. Additionally, when integrating the IMDB recommendation system, collaborative filtering is again at play. By leveraging cosine similarity scores between movie mentions in tweets and movies in the IMDB dataset, the model generates movie recommendations alongside product recommendations. This approach allows the model to offer personalized suggestions to users based on the preferences of similar users, effectively employing collaborative filtering techniques to enhance the recommendation process. Directed Multilayer Network In the proposed model, a directed multilayer network serves as the underlying framework for analyzing user-generated con- tent and delivering personalized recommendations based on sentiment analysis results and similarity scores. This network architecture comprises two distinct layers: the user-product layer and the product-product layer. The user-product layer represents the interactions between users and products, where nodes in this layer correspond to users and products, and directed edges denote user-product interactions, such as tweets mentioning specific products. Sentiment analysis of these interactions helps classify tweets as positive, negative, or neutral, facilitating a deeper under- standing of user sentiments towards various products. On the other hand, the product-product layer captures the relationships between different products based on their simi- larity scores. Each node in this layer represents a product, and edges signify the similarity between products, calculated using cosine similarity scores derived from the TF-IDF vectorization of tweet data. This layer enables the model to identify similar products based on their semantic connections in user-generated content. The directed multilayer network facilitates seamless integra- tion of sentiment analysis and similarity-based recommenda- tion techniques, allowing for a comprehensive analysis of user preferences and the generation of personalized recommenda- tions. By leveraging the interconnected nature of user-product interactions and product similarities, the model can effectively extract insights from social media content and deliver tailored recommendations to users, enhancing their overall experience. This directed multilayer network architecture provides a sophisticated and versatile framework for extracting valuable insights from user-generated content and delivering personal- ized recommendations in various domains, ranging from e- commerce to entertainment. III. EXPERIMENT In this section, the different correlation coefficient results are presented between sentiment ratings and IMDb movie ratings. We also describe a comprehensive quantitative and qualitative analysis, illustrating the efficacy and the precision of our proposed framework on movie database. A. Correlation between sentiment and IMDb movie ratings In analyzing the experimental results, particular attention is directed towards exploring the correlation between sentiment expressed in tweets and IMDb movie ratings. The process begins with the preparation of sentiment-labeled tweet data alongside IMDb movie ratings, ensuring their alignment for comparison. By extracting sentiment scores from tweets and aligning them with corresponding movie ratings based on shared mentions, statistical measures such as the Pearson correlation coefficient are computed to quantify the relation- ship. Interpretation of the correlation coefficient elucidates the strength and direction of this relationship, providing in- sights into how sentiment in tweets corresponds with IMDb ratings. Through meticulous analysis, the extent to which tweet sentiment correlates with IMDb ratings is scrutinized, shedding light on the efficacy of sentiment analysis in gauging user preferences. Discussion of the results encompasses the implications for recommendation systems, user experience enhancement, and potential limitations or biases within the data or analysis process. This analysis offers valuable insights into the interplay between user sentiment on social media and movie ratings, advancing understanding of how sentiment analysis can inform and optimize recommendation systems. B. Evaluation metric In evaluating the performance of the proposed model, an appropriate evaluation metric is essential to quantify its effec- tiveness in recommending similar products or movies based on sentiment analysis of user tweets. 1) TextBlob Approach : TextBlob itself doesn’t provide built-in evaluation metrics for sentiment analysis. However, since it outputs sentiment polarity (positive, negative, neutral) and subjectivity scores, we can use various external metrics to evaluate its performance on our specific task. Precision, recall, f1-score, support are some common metrics used. 2) SKLearn Approach : There are two types of classifiers in SKlearn approach namely, Bayesian classifiers and Non- bayesian classifiers. Bayesian classifiers We will compare the following bayesian algorithms using ROC curve: MultinomialNB BernoulliNB GaussianNB Non-Bayesian classifiers We will compare the following non-bayesian classifiers using ROC curve: Random Forest Logistic Regression Stochastic Gradient Descent Support Vector Machines Cross validation : The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it and to give an insight on how the model will generalize to an independent dataset. Sklearn provides stratified k-fold cross validation. This means each fold takes Figure 5. ROC curve comparison for Non-Bayesian classifiers the same proportion of each class as there exists in the whole dataset. IV. RESULT The e-commerce recommendation system outlined in this paper serves as a multifaceted tool aimed at enhancing user experience and driving sales. The proposed model is de- signed to automatically analyze user sentiments expressed in tweets related to specific products, enabling the generation of personalized recommendations. Leveraging the Tweepy API for tweet extraction and sentiment analysis tools like TextBlob or Sklearn, the system effectively identifies positive sentiments, thereby facilitating the recommendation of similar products to users. A key feature of this approach is its ability to integrate cross-validation techniques and hyperparameter tuning, ensuring the accuracy and reliability of the recommen- dation algorithm. Furthermore, the model’s integration with an external dataset, such as IMDB, allows for the provision of movie recommendations alongside product suggestions, broadening its utility and appeal. The authors emphasize the cost-effectiveness of this solution, underscoring its potential to enhance user engagement and drive sales for e-commerce platforms. However, it’s important to note a limitation in the system - it does not account for changes in user preferences or external factors that may influence purchasing decisions. To address this limitation, future enhancements could explore methods for dynamically updating the recommendation al- gorithm based on user feedback and market trends, thereby further refining the system’s performance and relevance in the ever-evolving e-commerce landscape. Declarations Author Contribution Joel Joy created a directed multilayer network to analyze E-commerce Recommender System. The network included separate layers for user-product interaction and product-product interactions, allowing me to capture the intricate interplay between these forces within the system.Akshay Raj K P and Rinu Raj T R collaborated on developing an innovative recommender system for IMDb that leverages Twitter sentiment analysis. Akshay Raj K P focused on building the sentiment analysis tool to understand user emotions from their tweets. The system then utilizes this information, along with Rinu Raj T R's work on the recommendation algorithm, to suggest similar movies to IMDb users. This approach provides a more nuanced recommendation experience compared to traditional methodsRini T Paul played a crucial role in overseeing and validating the progress of our project through guidance. Data Availability References:1.Getting clusters from structure data and attribute data. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining2.Link creation and profile alignment in the aNobii social network. In: 2010 IEEE Second International Conference on Social ComputingRest of the informations are collected using internet facility References Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A sur- vey of the state-of-the-art and pos- sible extensions. IEEE Transactions on Knowledge and Data Engi- neering 17(6):734–749 Sensors Journal ,Volume: 15, Issue: 8, August 2015,Page(s): 4313 - 4318. Combe D, Largeron C, Egyed-Zsigmond E, Gery M (2012) Getting clusters from structure data and attribute data. In: Proceedings of the International Conference on Advances in Social Net- works Analysis and Mining, ASONAM 2012, Istanbul, Turkey, 26-29 August, 2012, IEEE Com- puter Society, to appear Guy I, Ronen I, Wilcox E (2009) Do you know?: recommending people to invite into your social network. 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Yale University Press, New Haven, CT Bakshy E, Hofman JM, Mason WA, et al (2011) Everyone’s an influ- encer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. ACM, pp 65–74 Barrios F, Lo´pez F, Argerich L, et al (2015) Variations of the sim- ilarity function of TextRank for automated summarization. In: 2015 Argentine Symposium on Artificial Intelligence, Sociedad Argentina de Informa´tica e Investigacio´n Operativa (SADIO), pp 65–72 Bhavnani V, Galphat Y, Bhawsinghka G, et al (2021) A survey on detecting influential user in social networking. In: 2021 4th Biennial International Conference on Nascent Technologies in Engineering (IC- NTE), IEEE, pp 1–7 Additional Declarations No competing interests reported. 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INTRODUCTION","content":"\u003cp\u003eIn the world of online shopping, making customers happy and boosting sales is crucial. Personalized suggestions have been a big help in this, but imagine if we could go beyond the usual ways and use social media, like Twitter, for insights. Here, we can use a smart approach called directed multilayer networks to really understand what users like. This system doesn\u0026rsquo;t just look at what people buy; it also checks out their tweets, retweets, follows, and hashtag conversations. By putting all these interactions together, we can carefully figure out what users prefer. In this complex world, products become like connected dots, linked by what users talk about in tweets and retweets. Brands and hashtags become clues to finding out what users really want. This directed multilayer network isn\u0026rsquo;t just a flat map; it\u0026rsquo;s a living system that changes as users interact, creating new connections that reflect their changing preferences. This way, we\u0026rsquo;re not stuck looking at old snapshots of what users bought before. We can predict what they might want next and suggest products that match their evolving interests. This isn\u0026rsquo;t just a theory; it\u0026rsquo;s the future of online shopping, where suggestions come naturally from users\u0026rsquo; online\u003c/p\u003e \u003cp\u003elives. Every tweet and hashtag becomes a hint, guiding us to the perfect product users didn\u0026rsquo;t even know they wanted. So, get ready to explore this world of directed multilayer networks and change the way we do online shopping.\u003c/p\u003e \u003cp\u003eThe journey to build an online shopping suggestion system on Twitter using directed multilayer networks is a big goal, but it\u0026rsquo;s definitely doable. To make this happen, we need to mix technical skills, data know-how, and a good understanding of what users like. The potential benefits are clear \u0026ndash; a system that not only knows what users want but also helps build loyalty to brands, setting the stage for success in the ever- changing digital world. Directed Multilayer Networks (DMNs) aren\u0026rsquo;t just tools; they\u0026rsquo;re a way to see the world differently \u0026ndash; a way that uncovers hidden patterns and taps into the vast potential of connections. As the world gets more complicated, these networks are the key to figuring out how people behave, predicting trends, and shaping the future. Every whisper, every retweet, and every connection in these networks adds to our understanding, which keeps growing.\u003c/p\u003e \u003cp\u003eIn the complex world of online shopping, using directed multilayer networks doesn\u0026rsquo;t just boost technical abilities \u0026ndash; it\u0026rsquo;s a chance to understand how people interact. This cool approach gives us a wide view of what users do, going beyond the usual recommendation systems. The mix of purchases and social media interactions helps us really understand customers, making online shopping more personal. As the directed multi- layer network keeps changing with the ups and downs of what users like, it becomes proof of how technology and human understanding work together. This dynamic combo changes the idea of online shopping, turning it into a detailed experience where every tweet, retweet, and hashtag is like an important part of what customers want. The future of online shopping isn\u0026rsquo;t just about buying things; it\u0026rsquo;s a story shaped by online talks and the links in a directed multilayer network\u003c/p\u003e"},{"header":"II. PROPOSED MODEL","content":"\u003cp\u003eThe proposed model basically has two layers - one is user- product layer and other is product-product layer. This project basically consist of analyzing user\u0026rsquo;s tweet which are based on a particular product and recommending similar products based on that.Initially, tweets referencing specific products are collected from Twitter using the Tweepy API. These tweets\u003c/p\u003e\n\u003cp\u003eundergo rigorous text preprocessing to remove irrelevant con- tent such as retweets, links, hashtags, and mentions. Following this, sentiment analysis is performed using either TextBlob or Sklearn tools to classify each tweet as positive, negative, or neutral, based on its polarity. The cleaned and sentiment- labeled tweet data is then transformed into numerical vectors using TF-IDF vectorization. By calculating cosine similarity scores between these vectors, the model establishes connec- tions between tweets expressing positive sentiment towards products, identifying similar products based on their similarity scores.\u003c/p\u003e\n\u003cp\u003eTo enhance the model\u0026rsquo;s performance and ensure robustness, cross-validation techniques are employed, involving the divi- sion of the dataset into training and testing sets. Hyperpa- rameter tuning is conducted using methods like grid search or randomized search to optimize the model\u0026rsquo;s parameters, including vectorizer settings and similarity thresholds. Once trained and optimized, the model is exported for deployment in production environments, facilitating seamless integration into recommendation systems.\u003c/p\u003e\n\u003cp\u003eMoreover, the model\u0026rsquo;s versatility extends to the integra- tion of an IMDB recommendation system, where the SVC model and TF-IDF vectorizer trained on the IMDB dataset are imported. By leveraging cosine similarity scores between movie mentions in tweets and movies in the IMDB dataset, the model generates movie recommendations alongside product recommendations. These recommendations are then presented to users, offering a holistic and tailored approach to product and movie suggestions based on sentiment analysis results and similarity scores. Overall, the proposed model represents a sophisticated framework for extracting insights from user- generated content on social media and delivering personalized recommendations to users.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA. Data Acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed model aims to recommend similar products based on sentiment analysis of tweets mentioning specific products. Tweets are collected and preprocessed to remove noise like retweets and hashtags. Sentiment analysis is con- ducted to classify tweets as positive, negative, or neutral, and TF-IDF vectorization is applied to convert the text data into numerical vectors. Cosine similarity scores between these vectors are then calculated to identify connections between tweets expressing positive sentiment towards products, thus recommending similar products. Cross-validation techniques ensure model robustness, and hyperparameter tuning optimizes performance. The model\u0026rsquo;s versatility extends to integrating an IMDB recommendation system, leveraging cosine similarity scores between movie mentions in tweets and movies in the IMDB dataset to offer personalized recommendations. Overall, the model provides a sophisticated framework for extracting insights from user-generated content on social media and delivering tailored product and movie suggestions based on sentiment analysis and similarity scores.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. Data preprocessing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the proposed model, data preprocessing is pivotal for preparing the collected tweet data for subsequent analysis. Initially, tweets mentioning specific products are gathered from Twitter. However, these tweets frequently contain noise and extraneous content such as retweets, links, hashtags, and mentions. Therefore, a thorough process of text preprocessing is undertaken to cleanse the data.\u003c/p\u003e\n\u003cp\u003eThe preprocessing procedure encompasses several essential steps. Firstly, noise removal is executed to eliminate irrelevant elements from the tweet text, focusing solely on the pertinent content related to the products of interest. Following this, tokenization divides the cleaned tweet text into individual tokens or words, facilitating further analysis by breaking down the text into manageable units.\u003c/p\u003e\n\u003cp\u003eNormalization is then applied to the text data to ensure con- sistency, often involving converting text to lowercase to avoid duplicate representations of words with different cases. Ad- ditionally, stopwords\u0026mdash;commonly occurring words that lack significant meaning\u0026mdash;are removed to reduce noise and enhance the efficiency of downstream tasks.\u003c/p\u003e\n\u003cp\u003eFurther steps include stemming or lemmatization, which reduces words to their root form, aiding in consolidating different variations of the same word and thereby minimizing vocabulary size and enhancing model performance. Special at- tention is paid to emoticons and abbreviations commonly used in social media text to ensure their meaningful representation in the data.\u003c/p\u003e\n\u003cp\u003eLastly, handling special characters and encoding techniques are applied to ensure compatibility with subsequent analysis and modeling steps. By meticulously conducting these preprocessing steps, the tweet data is cleansed and structured into a suitable format for sentiment analysis and vectorization. This ensures that subsequent analyses are based on high- quality, relevant data, ultimately leading to more accurate recommendations for users.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC. Twitter Sentiment Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwitter has become very popular since the release in 2006 and is a social media platform where people can easily put\u003c/p\u003e\n\u003cp\u003emicro blogs, the so-called tweets, on their online Twitter profile. In doing so it provides to be a great source for textual data and personal opinions about all kinds of different topics. The sentiment analyses of these tweets can be considered as a specific topic within the area of sentiment analysis (Zimbra et al. 2018). Various research has been conducted where sentiment analyses are performed on tweets. Asur and Huberman (2010) were able to find how the use of sentiment analysis on tweets can create more accurate forecasts. The insights gained with these Twitter sentiment analyses can lead to interesting outcomes that can be used in areas such as marketing or social studies (Zhang et al. 2011). With Twitter sentiment analysis several challenges come into play. One of these challenges is the use of emoticons in tweets. Lexicon- based approaches are not always able to detect emoticons and they used to get removed from the text (Hogenboom et al. 2013). Research conducted by Hogenboom et al. (2013) found a significant increase in accuracy when emoticons are taken into account. Not only emoticons are used in tweets but also abbreviations, such as acronyms and initialisms, are very common in tweets since a tweet cannot exceed the limit of 280 characters. In earlier research by Kouloumpis, Wilson, and Moore (2011) it was proven useful to make use of emoticons and abbreviations when sentiment analysis is performed on tweets. Therefore, emoticons and abbreviations should both be taken into account when analysing the sentiment of a tweet. In addition to abbreviations and emoticons tweets can also contain slang. Singh and Kumari (2016) found that the usage of slang words in sentiment analysis leads to a better sentiment classification. Thus it is important that slang within tweets should be taken into consideration when a Twitter sentiment analysis is performed. Various sentiment analysis methods can be performed to find the sentiment polarity score of a certain document, text or sentence. In this study both TextBlob and VADER are used for this task. In the subsections below these methods will be clarified.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTextBlob\u003c/em\u003e: TextBlob can be classified as a lexicon-based approach when used for sentiment analysis. Next to sentiment analysis, Textblob can be used to perform numerous natural language processing tasks which include translation, noun phrase extraction, partof-speech tagging, spelling correction, word and phrase frequencies, and more. Two different sen- timent analysis methods can be utilized by TextBlob. The default method is based on the pattern library and the other method is a Naive Bayes analyzer which has its roots in NLTK (Loria 2018). This study uses the former of the two. The default sentiment analysis function returns a polarity score between \u0026minus;\u0026thinsp;1.0 and 1. Numbers above zero can be seen as positive, at or close to zero as neutral, and below zero as negative.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eD. Collaborative filtering\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWithin recommendation systems, collaborative filtering serves as a method to predict or recommend items for users, drawing insights from the preferences of comparable users. In\u003c/p\u003e\n\u003cp\u003ethe envisioned model, this technique is implicitly employed within the product-product layer.\u003c/p\u003e\n\u003cp\u003eWhen calculating cosine similarity scores between the nu- merical vectors representing tweets expressing positive senti- ment towards products, the model is essentially measuring the similarity between different products based on how often they are mentioned together in positive tweets. This is a form of item-based collaborative filtering, where the model identifies products that tend to be liked or recommended by users who have expressed positive sentiment towards similar products.\u003c/p\u003e\n\u003cp\u003eAdditionally, when integrating the IMDB recommendation system, collaborative filtering is again at play. By leveraging cosine similarity scores between movie mentions in tweets and movies in the IMDB dataset, the model generates movie recommendations alongside product recommendations. This approach allows the model to offer personalized suggestions to users based on the preferences of similar users, effectively employing collaborative filtering techniques to enhance the recommendation process.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDirected Multilayer Network\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the proposed model, a directed multilayer network serves as the underlying framework for analyzing user-generated con- tent and delivering personalized recommendations based on sentiment analysis results and similarity scores. This network architecture comprises two distinct layers: the user-product layer and the product-product layer.\u003c/p\u003e\n\u003cp\u003eThe user-product layer represents the interactions between users and products, where nodes in this layer correspond to users and products, and directed edges denote user-product interactions, such as tweets mentioning specific products. Sentiment analysis of these interactions helps classify tweets as positive, negative, or neutral, facilitating a deeper under- standing of user sentiments towards various products.\u003c/p\u003e\n\u003cp\u003eOn the other hand, the product-product layer captures the relationships between different products based on their simi- larity scores. Each node in this layer represents a product, and edges signify the similarity between products, calculated using cosine similarity scores derived from the TF-IDF vectorization of tweet data. This layer enables the model to identify similar products based on their semantic connections in user-generated content.\u003c/p\u003e\n\u003cp\u003eThe directed multilayer network facilitates seamless integra- tion of sentiment analysis and similarity-based recommenda- tion techniques, allowing for a comprehensive analysis of user preferences and the generation of personalized recommenda- tions. By leveraging the interconnected nature of user-product interactions and product similarities, the model can effectively extract insights from social media content and deliver tailored recommendations to users, enhancing their overall experience. This directed multilayer network architecture provides a sophisticated and versatile framework for extracting valuable insights from user-generated content and delivering personal- ized recommendations in various domains, ranging from e-\u003c/p\u003e\n\u003cp\u003ecommerce to entertainment.\u003c/p\u003e"},{"header":"III. EXPERIMENT","content":"\u003cp\u003eIn this section, the different correlation coefficient results are presented between sentiment ratings and IMDb movie ratings. We also describe a comprehensive quantitative and qualitative analysis, illustrating the efficacy and the precision of our proposed framework on movie database.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cem\u003eA. Correlation between sentiment and IMDb movie ratings\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn analyzing the experimental results, particular attention is directed towards exploring the correlation between sentiment expressed in tweets and IMDb movie ratings. The process begins with the preparation of sentiment-labeled tweet data alongside IMDb movie ratings, ensuring their alignment for comparison. By extracting sentiment scores from tweets and aligning them with corresponding movie ratings based on shared mentions, statistical measures such as the Pearson correlation coefficient are computed to quantify the relation- ship. Interpretation of the correlation coefficient elucidates the strength and direction of this relationship, providing in- sights into how sentiment in tweets corresponds with IMDb ratings. Through meticulous analysis, the extent to which tweet sentiment correlates with IMDb ratings is scrutinized, shedding light on the efficacy of sentiment analysis in gauging user preferences. Discussion of the results encompasses the implications for recommendation systems, user experience enhancement, and potential limitations or biases within the data or analysis process. This analysis offers valuable insights into the interplay between user sentiment on social media and movie ratings, advancing understanding of how sentiment analysis can inform and optimize recommendation systems.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cem\u003eB. Evaluation metric\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn evaluating the performance of the proposed model, an appropriate evaluation metric is essential to quantify its effec-\u003c/p\u003e\n\u003cp\u003etiveness in recommending similar products or movies based on sentiment analysis of user tweets.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cem\u003e1) TextBlob Approach\u003c/em\u003e: TextBlob itself doesn\u0026rsquo;t provide built-in evaluation metrics for sentiment analysis. However, since it outputs sentiment polarity (positive, negative, neutral) and subjectivity scores, we can use various external metrics to evaluate its performance on our specific task. Precision, recall, f1-score, support are some common metrics used.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e2) SKLearn Approach\u003c/em\u003e: There are two types of classifiers in SKlearn approach namely, Bayesian classifiers and Non- bayesian classifiers.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBayesian classifiers\u003c/p\u003e\n\u003cp\u003eWe will compare the following bayesian algorithms using ROC curve:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMultinomialNB\u003c/li\u003e\n \u003cli\u003eBernoulliNB\u003c/li\u003e\n \u003cli\u003eGaussianNB\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNon-Bayesian classifiers\u003c/p\u003e\n\u003cp\u003eWe will compare the following non-bayesian classifiers using ROC curve:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eRandom Forest\u003c/li\u003e\n \u003cli\u003eLogistic Regression\u003c/li\u003e\n \u003cli\u003eStochastic Gradient Descent\u003c/li\u003e\n \u003cli\u003eSupport Vector Machines\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eCross validation\u003c/em\u003e: The goal of cross-validation is to test the model\u0026rsquo;s ability to predict new data that was not used in estimating it and to give an insight on how the model will generalize to an independent dataset. Sklearn provides stratified k-fold cross validation. This means each fold takes\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;5. ROC curve comparison for Non-Bayesian classifiers\u003c/p\u003e\n\u003cp\u003ethe same proportion of each class as there exists in the whole dataset.\u003c/p\u003e"},{"header":"IV. RESULT","content":"\u003cp\u003eThe e-commerce recommendation system outlined in this paper serves as a multifaceted tool aimed at enhancing user experience and driving sales. The proposed model is de- signed to automatically analyze user sentiments expressed in tweets related to specific products, enabling the generation of personalized recommendations. Leveraging the Tweepy API for tweet extraction and sentiment analysis tools like TextBlob or Sklearn, the system effectively identifies positive sentiments, thereby facilitating the recommendation of similar products to users. A key feature of this approach is its ability\u003c/p\u003e\n\u003cp\u003eto integrate cross-validation techniques and hyperparameter tuning, ensuring the accuracy and reliability of the recommen- dation algorithm. Furthermore, the model\u0026rsquo;s integration with an external dataset, such as IMDB, allows for the provision of movie recommendations alongside product suggestions, broadening its utility and appeal. The authors emphasize the cost-effectiveness of this solution, underscoring its potential to enhance user engagement and drive sales for e-commerce platforms. However, it\u0026rsquo;s important to note a limitation in the system - it does not account for changes in user preferences or external factors that may influence purchasing decisions. To address this limitation, future enhancements could explore methods for dynamically updating the recommendation al- gorithm based on user feedback and market trends, thereby further refining the system\u0026rsquo;s performance and relevance in the ever-evolving e-commerce landscape.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJoel Joy created a directed multilayer network to analyze E-commerce Recommender System. The network included separate layers for user-product interaction and product-product interactions, allowing me to capture the intricate interplay between these forces within\u0026nbsp;the\u0026nbsp;system.Akshay Raj K P and Rinu Raj T R collaborated on developing an innovative recommender system for IMDb that leverages Twitter sentiment analysis. Akshay Raj K P focused on building the sentiment analysis tool to understand user emotions from their tweets. The system then utilizes this information, along with Rinu Raj T R's work on the recommendation algorithm, to suggest similar movies to IMDb users. This approach provides a more nuanced recommendation experience compared to traditional\u0026nbsp;methodsRini T Paul played a crucial role in overseeing and validating the progress of our project through guidance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eReferences:1.Getting clusters from structure data and attribute data. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining2.Link creation and profile alignment in the aNobii social network. In: 2010 IEEE Second International Conference on Social ComputingRest of the informations are collected using internet facility\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A sur- vey of the state-of-the-art and pos- sible extensions. IEEE Transactions on Knowledge and Data Engi- neering 17(6):734\u0026ndash;749 Sensors Journal ,Volume: 15, Issue: 8, August 2015,Page(s): 4313 - 4318.\u003c/li\u003e\n\u003cli\u003eCombe D, Largeron C, Egyed-Zsigmond E, Gery M (2012) Getting clusters from structure data and attribute data. In: Proceedings of the International Conference on Advances in Social Net- works Analysis and Mining, ASONAM 2012, Istanbul, Turkey, 26-29 August, 2012, IEEE Com- puter Society, to appear\u003c/li\u003e\n\u003cli\u003eGuy I, Ronen I, Wilcox E (2009) Do you know?: recommending people to invite into your social network. In: Conati C, Bauer M, Oliver N, Weld DS (eds) Proceedings of the 2009 International Conference on Intelligent User Interfaces (IUI), February 8-11, 2009, Sanibel Island, Florida, USA, ACM, pp 77\u0026ndash;86\u003c/li\u003e\n\u003cli\u003eDomingos P (2005) Mining social networks for viral marketing. IEEE Intelligent Systems 20(1):80\u0026ndash; 82\u003c/li\u003e\n\u003cli\u003eGetoor L (2010) Link mining and link discovery. In: Encyclopedia of Machine Learning, pp 606\u0026ndash;609\u003c/li\u003e\n\u003cli\u003eLi B, King I (2010) Routing questions to appropriate answerers in community question answering services. In: Huang J, Koudas N, Jones GJF, Wu X, Collins-Thompson K, An A (eds) Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010, ACM, pp 1585\u0026ndash;1588\u003c/li\u003e\n\u003cli\u003eLin CY, Cao N, Liu S, Papadimitriou S, Sun J, Yan X (2009) Smallblue: Social network analysis for expertise search and collective intelligence. In: Ioannidis YE, Lee DL, Ng RT (eds) Proceedings of the 25th International Conference on Data Engineering, ICDE 2009, March 29 2009 - April 2 2009, Shanghai, China, IEEE, pp 1483\u0026ndash;1486\u003c/li\u003e\n\u003cli\u003eStan J, Do VH, Maret P (2011) Semantic user interaction profiles for better people recommenda- tion. In: International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, Kaohsiung, Taiwan, 25-27 July 2011, IEEE Computer Society, pp 434\u0026ndash;437\u003c/li\u003e\n\u003cli\u003eWasserman S, Faust K (1994) Social Network Analysis: Methods and Applications, 4th edn. No. 8 in Structural analysis in the social sciences, Cambridge University Press, New York, USA\u003c/li\u003e\n\u003cli\u003eAhuja RK, Magnanti TL, Orlin JB (1993) Network flows. Prentice-Hall Inc., Upper Saddle River, NJ\u003c/li\u003e\n\u003cli\u003eAiello LM, Barrat A, Cattuto C, et al (2010) Link creation and profile alignment in the aNobii social network. In: 2010 IEEE Second International Conference on Social Computing, IEEE, pp 249\u0026ndash;256\u003c/li\u003e\n\u003cli\u003eAllard K (1990) Command, control, and the common defense. Yale University Press, New Haven, CT\u003c/li\u003e\n\u003cli\u003eBakshy E, Hofman JM, Mason WA, et al (2011) Everyone\u0026rsquo;s an influ- encer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. ACM, pp 65\u0026ndash;74\u003c/li\u003e\n\u003cli\u003eBarrios F, Lo\u0026acute;pez F, Argerich L, et al (2015) Variations of the sim- ilarity function of TextRank for automated summarization. In: 2015 Argentine Symposium on Artificial Intelligence, Sociedad Argentina de Informa\u0026acute;tica e Investigacio\u0026acute;n Operativa (SADIO), pp 65\u0026ndash;72\u003c/li\u003e\n\u003cli\u003eBhavnani V, Galphat Y, Bhawsinghka G, et al (2021) A survey on detecting influential user in social networking. In: 2021 4th Biennial International Conference on Nascent Technologies in Engineering (IC- NTE), IEEE, pp 1\u0026ndash;7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DMN, NCF","lastPublishedDoi":"10.21203/rs.3.rs-4223941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4223941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn E-commerce recommender system analyzes user behavior and preferences to suggest personalized product rec- ommendations, enhancing the shopping experience and boosting sales by presenting items likely to interest individual users. Ecommerce recommender system is integrating a directed mul- tilayer network analysis to optimize product recommendations on Twitter. Our methodology involves modeling user interac- tions as a weighted, directed network through Social Network Analysis, providing insights into influential users and dynamic group dynamics. The directed multilayer network approach is implemented by connecting users (one layer) to the conversations and topics (a second layer) they engage in, with inter-layer con- nections reflecting user participation in conversations. Building upon this, we introduce an innovative e-commerce recommenda- tion system that triggers personalized product suggestions to a user\u0026rsquo;s friends based on their purchase history. This integration harnesses the wealth of self-reported information on Twitter, delivering targeted and socially-informed e-commerce recommen- dations. The study establishes a robust framework, marrying social network insights with an e-commerce recommender system for effective and personalized product promotion within online communities\u003c/p\u003e","manuscriptTitle":"E-commerce Recommender System on Twitter using Directed Multilayer Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:30:02","doi":"10.21203/rs.3.rs-4223941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e53cd50-e554-405f-b9ba-f7fffce3a3ba","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-19T18:30:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:30:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4223941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4223941","identity":"rs-4223941","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}