情報検索における意味的ギャップの解消 : トピックモデルを用いた先進的画像探索

Tohoku University徳山豪課

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

Ranking of high-value social audiences on Twitter

Even though social media offers plenty of business opportunities, for a company to identify the right audience from the massive amount of social media data is highly challenging given finite resources and marketing budgets. In this paper, we present a ranking mechanism that is capable of identifying the top-k social audience members on Twitter based on an index. Data from three different Twitter business account owners were used in our experiments to validate this ranking mechanism. The results show that the index developed using a combination of semi-supervised and supervised learning methods is indeed generic enough to retrieve relevant audience members from the three different data sets. This approach of combining Fuzzy Match, Twitter Latent Dirichlet Allocation and Support Vector Machine Ensemble is able to leverage on the content of account owners to construct seed words and training data sets with minimal annotation efforts. We conclude that this ranking mechanism has the potential to be adopted in real-world applications for differentiating prospective customers from the general audience and enabling market segmentation for better business decision making

Identifying the high-value social audience from Twitter through text-mining methods

Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of the account owner to segment the followers and identify a group of high-value social audience members. This enables the account owner to spend resources more effectively by sending offers to the right audience and hence maximize marketing efficiency and improve the return of investment

Topic modeling in marketing: recent advances and research opportunities

Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models

Competitive analysis of online reviews using exploratory text mining

Purpose – This paper explores the usefulness of analyzing text-based online reviews using text mining tools and visual analytics for SWOT Analysis, as applied to the hotel industry. These results can be used to develop competitive actions. Design – The text mining/visualization tool, ReviewMap, was used to transform an archive of reviews spanning multiple suppliers into a hierarchy of data of increasing dimensionality. Visual summaries at each level were integrated to propagate selections at one level throughout the rest of the hierarchy. These visual summaries identify features required for competition at a given level and features that currently discriminate amongst competitors. Methodology – The approach was exploratory, the objective of which was to determine if useable competitive intelligence could be found in a typical collection of online reviews for a set of competing hotels. A publically available collection of reviews was subjected to a set of text mining procedures and visual analyses in order to summarize the features and opinions expressed. Originality – Prior analyses of online reviews relied solely upon numeric “star” ratings. This study utilized text mining to uncover information within the written comments and applied the information in a SWOT Analysis of three competing hotels. Findings – In the set of reviews used in this paper, a common measure of analytical power almost doubled when text mining summaries of the written comments were used in combination with numeric ratings. Visual analytics revealed the dominant features for each hotel, the features required of all hotels competing at a given level, and the features that define specific positions within the competitive landscape. This analysis of strengths, weaknesses, opportunities and threats revealed several promising competitive actions for the hotels in the study

Reklamos internete vartotojų segmentavimas taikant latentinį Dirichlė paskirstymo modelį

User segmentation is one the most important problems in online advertiting. The use of online latent Dirichlet allocation model for analysing big datasets for this purpose is proposed in this paper. The relationship between the number of segments and segmentation quality metrics is analized and criteria for assigning ads to user segments are proposed.Vartotojų segmentavimas yra vienas iš aktualiausių reklamos internete uždavinių. Darbe šio uždavinio sprendimui siūloma taikyti adaptyvų latentinį Dirichlė paskirstymo modelį, kuris tinka ir didžiųjų duomenų rinkinių analizei. Pasiūlyti reklamų priskyrimo segmentams kriterijai ir ištirtos segmentavimo kokybės metrikų priklausomybės nuo segmentų skaičiaus