Attention-based learning of self-media data for marketing intention detection

People’s daily information sharing and acquisition through the Internet has become more and more popular. The comprehensive multimodal marketing advertorial generated by ‘We Media’ accounts besides the normal social news is gaining its importance on social media platforms. In order to achieve effective advertising, the marketing intent understanding is a key step towards generating targeted advertising strategies (push advertorials to specific people at a specific time). However, advertorials in real are usually designed to pretend as normal social news with a wide range of contents. This poses big challenges to the platforms on accurately recognizing and analyzing the marketing intents behind the advertorials. As a pioneering study, we address this new problem of multimodalbased marketing intent analysis and answer three core questions: (1) does a piece of social news contain marketing intent? (2) what is the topic of marketing intent? (3) what is the extent of marketing intent? Towards this end, we propose a novel Multimodal-based Marketing Intent Analysis scheme (MMIA) to estimate the marketing intent embedded in the multimodal contents. Specifically, a novel supervised neural autoregressive model (SmiDocNADE) is proposed to enhance the discriminative capacity of the learned hidden features so that a single system is capable of solving the three questions. In order to effectively model inter-correlations between images and text in advertorials, we fuse multimodal data and extract features by Graph Convolution Networks as an enhancement to SmiDocNADE. The extensive evaluations demonstrate the advantages of our proposed system in multimodal-based marketing intent analysis from multiple aspects.

/pdf/multimodal-marketing-intent-analysis-for-effective-targeted-5cm4gzl06o.pdf

Multimodal Marketing Intent Analysis for Effective Targeted Advertising

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors’ opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term “opportunities” that are akin to “anticipation” in Plutchik’s theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors’ decision making.

/pdf/detection-of-financial-opportunities-in-micro-blogging-data-2mal5y153m.pdf

Detection of Financial Opportunities in Micro-Blogging Data With a Stacked Classification System

A CLSTM-TMN for marketing intention detection

To solve the problem that the features produced by hidden means, such as code obfuscation and compression, in encrypted malicious WebShell files are not the same as those produced by non-encrypted files, a WebShell attack detection algorithm based on ensemble learning is proposed. First, this algorithm extracted the feature vocabulary of the unigrams and 4-grams based on opcode; subsequently, the 4-gram feature word weights were obtained according to the calculated Gini coefficient of the unigram feature words and used to select the features, which will be selected again based on the Gini coefficient of the 4-gram feature words. Consequently, a feature vocabulary that can detect encrypted and unencrypted WebShell files was constructed. Second, in order to improve the adaptability and accuracy of the detection method, an ensemble detection model called WS-LSMR, consisting of a Logistic Regression, Support Vector Machine, Multi-layer Perceptron and Random Forest, was constructed. The model uses a weighted voting method to determine the WebShell classification. This experiment demonstrated that compared with the traditional single WebShell detection algorithm, the recall rate and accuracy rate improved to 99.14% and 94.28%, respectively, which proves that this method has better detection performance.

/pdf/ws-lsmr-malicious-webshell-detection-algorithm-based-on-4ireb4tasv.pdf

WS-LSMR: Malicious WebShell Detection Algorithm Based on Ensemble Learning

Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.

https://www.mdpi.com/1999-5903/11/7/155/pdf

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Paper influence analysis is essential technology of the literature management system. In recent years, research on influence analysis has received crucial attention from the industry and academia. In this field, there are many new challenges in how to evaluate valuable papers in the paper citation network. Therefore, in this paper, we have proposed an influence model in paper citation networks with integration features of PageRank and Hyperlink-Induced Topic Search (HITS). First, three ranking features are extracted with the combination of PageRank value, hub, and authority. Second, more features are added to our ranking classification method. Third, the ranking-based classification with $P$ ageRank and HITS is proposed to analyze the paper propagation. An influence score is computed to evaluate the importance of a paper. The experiments show that our ranking-based influence model is effective for paper propagation.

Influence Model of Paper Citation Networks with Integrated PageRank and HITS

Sentiment classification is a hot research direction at present, but most research is based on balanced data sets. In real life, the sample is impossible to balance. For sentiment analysis of unbalanced data, we not only need to pay attention to the overall classification performance, but also need to care about the classification performance of a few classes. How to improve the recognition rate of a few types of samples while improving the overall recognition rate has become a research hotspot. Aiming at this problem, this paper proposes a model based on ensemble learning, extracts features by TF-IDF+SVD, and integrates five base classifiers by stacking to sentiment classification. The experimental results show that it can be more effective in emotional classification on unbalanced data sets than other methods.

Jidong Duan

Papers

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Influence Model of Paper Citation Networks with Integrated PageRank and HITS

Unbalanced data sentiment classification method based on ensemble learning